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.     

An informative Bayesian structural equation model to assess source-specific health effects of air pollution

A primary objective of current air pollution research is the assessment of health effects related to specific sources of air particles or particulate matter (PM). Quantifying source-specific risk is a challenge because most PM health studies do not directly observe the contributions of the pollution sources themselves. Instead, given knowledge of the chemical characteristics of known sources, investigators infer pollution source contributions via a source apportionment or multivariate receptor analysis applied to a large number of observed elemental concentrations. Although source apportionment methods are well established for exposure assessment, little work has been done to evaluate the appropriateness of characterizing unobservable sources thus in health effects analyses. In this article, we propose a structural equation framework to assess source-specific health effects using speciated elemental data. This approach corresponds to fitting a receptor model and the health outcome model jointly, such that inferences on the health effects account for the fact that uncertainty is associated with the source contributions. Since the structural equation model (SEM) typically involves a large number of parameters, for small-sample settings, we propose a fully Bayesian estimation approach that leverages historical exposure data from previous related exposure studies. We compare via simulation the performance of our approach in estimating source-specific health effects to that of 2 existing approaches, a tracer approach and a 2-stage approach. Simulation results suggest that the proposed informative Bayesian SEM is effective in eliminating the bias incurred by the 2 existing approaches, even when the number of exposures is limited. We employ the proposed methods in the analysis of a concentrator study investigating the association between ST-segment, a cardiovascular outcome, and major sources of Boston PM and discuss the implications of our findings with respect to the design of future PM concentrator studies.     

Access rate to the emergency department for venous thromboembolism in relationship with coarse and fine particulate matter air pollution

Particulate matter (PM) air pollution has been associated with cardiovascular and respiratory disease. Recent studies have proposed also a link with venous thromboembolism (VTE) risk. This study was aimed to evaluate the possible influence of air pollution-related changes on the daily flux of patients referring to the Emergency Department (ED) for VTE, dissecting the different effects of coarse and fine PM. From July 1(st), 2007, to June 30(th), 2009, data about ED accesses for VTE and about daily concentrations of PM air pollution in Verona district (Italy) were collected. Coarse PM (PM(10-2.5)) was calculated by subtracting the finest PM(2.5) from the whole PM(10). During the index period a total of 302 accesses for VTE were observed (135 males and 167 females; mean age 68.3 ± 16.7 years). In multiple regression models adjusted for other atmospheric parameters PM(10-2.5), but not PM(2.5), concentrations were positively correlated with VTE (beta-coefficient = 0.237; P = 0.020). During the days with high levels of PM(10-2.5) (≥ 75(th) percentile) there was an increased risk of ED accesses for VTE (OR 1.69 with 95%CI 1.13-2.53). By analysing days of exposure using distributed lag non-linear models, the increase of VTE risk was limited to PM(10-2.5) peaks in the short-term period. Consistently with these results, in another cohort of subjects without active thrombosis (n = 102) an inverse correlation between PM(10-2.5) and prothrombin time was found (R = -0.247; P = 0.012). Our results suggest that short-time exposure to high concentrations of PM(10-2.5) may favour an increased rate of ED accesses for VTE through the induction of a prothrombotic state.     

Risk of adult street vendor exposure to traffic-related air pollution in Bangkok,Thailand

Traffic-related air pollution (TRAP), including particulate matter (PM) in respirable coarse and fine size fractions (PM10 and PM2.5), is known to have exposure effects on human health and environment. Real-time PM10 and PM2.5 concentrations were collected from the study locations in Bangkok, Thailand, using TSI AM510 particle counters. Temperature and % relative humidity (%RH) were also collected. Data were compared to data from the closest station of the Pollution Control Department (PCD), Thailand. Real-time mean concentration varied from 86 to 1107 µg/m³ (PM10) and varied from 25 to 664 µg/m³ (PM2.5). In addition, real-time mean PM10 (223.1 µg/m³) was nearly four times greater than that measured by the PCD station, 60 µg/m³. Temperature and %RH from real-time air monitoring and PCD station were comparable. In each study location (five locations, two in morning and afternoon/evening), there were significant positive correlations between PM10 and PM2.5 concentrations and significant negative correlations between temperature and RH%. Results suggested that outdoor TRAP via measured real-time PM concentrations were more realistic exposure concentration estimates among street vendors as related to respiratory and other symptoms than data obtained from PCD station. Nevertheless, PM10 as measured by the PCD station might be a reasonable surrogate for estimated outdoor PM2.5 exposure.     

Hierarchical bivariate time series models: a combined analysis of the effects of particulate matter on morbidity and mortality

In this paper we develop a hierarchical bivariate time series model to characterize the relationship between particulate matter less than 10 microns in aerodynamic diameter (PM10) and both mortality and hospital admissions for cardiovascular diseases. The model is applied to time series data on mortality and morbidity for 10 metropolitan areas in the United States from 1986 to 1993. We postulate that these time series should be related through a shared relationship with PM10. At the first stage of the hierarchy, we fit two seemingly unrelated Poisson regression models to produce city-specific estimates of the log relative rates of mortality and morbidity associated with exposure to PM10 within each location. The sample covariance matrix of the estimated log relative rates is obtained using a novel generalized estimating equation approach that takes into account the correlation between the mortality and morbidity time series. At the second stage, we combine information across locations to estimate overall log relative rates of mortality and morbidity and variation of the rates across cities. Using the combined information across the 10 locations we find that a 10 microg/m3 increase in average PM10 at the current day and previous day is associated with a 0.26% increase in mortality (95% posterior interval -0.37, 0.65), and a 0.71% increase in hospital admissions (95% posterior interval 0.35, 0.99). The log relative rates of mortality and morbidity have a similar degree of heterogeneity across cities: the posterior means of the between-city standard deviations of the mortality and morbidity air pollution effects are 0.42 (95% interval 0.05, 1.18), and 0.31 (95% interval 0.10, 0.89), respectively. The city-specific log relative rates of mortality and morbidity are estimated to have very low correlation, but the uncertainty in the correlation is very substantial (posterior mean = 0.20, 95% interval -0.89, 0.98). With the parameter estimates from the model, we can predict the hospitalization log relative rate for a new city for which hospitalization data are unavailable, using that city's estimated mortality relative rate. We illustrate this prediction using New York as an example.     

Linking exposure to environmental pollutants with biological effects

Exposure to ambient air pollution has been associated with cancer. Ambient air contains a complex mixture of toxics, including particulate matter (PM) and benzene. Carcinogenic effects of PM may relate both to the content of PAH and to oxidative DNA damage generated by transition metals, benzene, metabolism and inflammation. By means of personal monitoring and biomarkers of internal dose, biologically effective dose and susceptibility, it should be possible to characterize individual exposure and identify air pollution sources with relevant biological effects. In a series of studies, individual exposure to PM(2.5), nitrogen dioxide (NO(2)) and benzene has been measured in groups of 40-50 subjects. Measured biomarkers included 1-hydroxypyrene, benzene metabolites (phenylmercapturic acid (PMA) and trans-trans-muconic acid (ttMA)), 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) in urine, DNA strand breaks, base oxidation, 8-oxodG and PAH bulky adducts in lymphocytes, markers of oxidative stress in plasma and genotypes of glutathione transferases (GSTs) and NADPH:quinone reductase (NQO1). With respect to benzene, the main result indicates that DNA base oxidation is correlated with PMA excretion. With respect to exposure to PM, biomarkers of oxidative damage showed significant positive association with the individual exposure. Thus, 8-oxodG in lymphocyte DNA and markers of oxidative damage to lipids and protein in plasma associated with PM(2.5) exposure. Several types of DNA damage showed seasonal variation. PAH adduct levels, DNA strand breaks and 8-oxodG in lymphocytes increased significantly in the summer period, requiring control of confounders. Similar seasonal effects on strand breaks and expression of the relevant DNA repair genes ERCC1 and OGG1 have been reported.In the present setting, biological effects of air pollutants appear mainly related to oxidative stress via personal exposure and not to urban background levels. Future developments include personal time-resolved monitors for exposure to ultrafine PM and PM(2.5,) use of GPS, as well as genomics and proteomics based biomarkers.     

The lag-effect pattern in the relationship of particulate air pollution to daily mortality in Seoul,Korea

To assess differences in the lag-effect pattern in the relationship between particulate matter less than 10 microm in aerodynamic diameter (PM(10)) and cause-specific mortality in Seoul, Korea, from January 1995 to December 1999, we performed a time-series analysis. We used a generalized additive Poisson regression model to control for time trends, temperature, humidity, air pressure, and the day of the week. The PM(10) effect was estimated on the basis of the time-series models using the 24-h means and the quadratic distributed-lag models using a cumulative 6-day effect. One interquartile range increase in the 6-day cumulative mean of PM(10) (43.12 microg/m(3)) was associated with an increase in non-accidental deaths [3.7%, 95% confidence interval (CI): 2.1, 5.4], respiratory disease (13.9%, 95% CI: 6.8, 21.5), cardiovascular disease (4.4%, 95% CI: -1.0, 9.0), and cerebrovascular disease (6.3%, 95% CI: 2.3, 10.5). We found the following patterns in the disease-specific lag-effect window: respiratory mortality was more affected by air pollution level on the day of death, whereas cardiovascular deaths were more affected by the previous day's air pollution level. Cerebrovascular deaths were simultaneously associated with the air pollution levels of the same day and the previous day. The patterns in the lag effect from the distributed-lag models were similar to those of a series of time-series models with 24-h means. These results contribute to our understanding of how exposure to air pollution causes adverse health effects.     

Health-exposure modeling and the ecological fallacy

Recently, there has been an increased interest in modeling the association between aggregate disease counts and environmental exposures measured, for example via air pollution monitors, at point locations. This paper has two aims: first, we develop a model for such data in order to avoid ecological bias; second, we illustrate that modeling the exposure surface and estimating exposures may lead to bias in estimation of health effects. Design issues are also briefly considered, in particular the loss of information in moving from individual to ecological data, and the at-risk populations to consider in relation to the pollution monitor locations. The approach is investigated initially through simulations, and is then applied to a study of the association between mortality in those over 65 in the year 2000 and the previous year's SO2, in London. We conclude that the use of the proposed model can provide valid inference, but the use of estimated exposures should be carried out with great caution.     

Cytotoxicity of PM(2.5) and PM(2.5--10) ambient air pollutants assessed by the MTT and the Comet assays

Ambient air particulate matters are classified into two distinct modes in size distribution, namely the coarse and fine particles. Correlation between high particulate concentration and adverse effects on human populations has long been recognized, however, the toxicology of these adverse effects has not been clarified. In the current report, the cytotoxic effects of the solvent-extractable organic compounds (SEOC) from fine particles smaller than 2.5 microm (PM(2.5)) and from coarse particles between 2.5-10 microm (PM(2.5-10)) were studied. Nine 24h consecutive monthly samples were tested to determine the correlation between cytotoxicity and total SEOC in two size fractions of particulate air pollution. Cytotoxicity of SEOC was measured by two micro-scale mammalian cells-based bioassays: the MTT cell proliferation assay, and the Comet assay for the detection of DNA damage. A well-defined mammalian cell line - Rat 6 rodent fibroblast was employed in the study. The SEOC extracts of air particulate matters were sub divided into two equal parts. One part was dissolved in DMSO, the other in KOH/hexane and then conjugated with bovine serum albumin to produce a lipid-soluble fraction for testing. The DMSO fraction would contain mainly the polycyclic aromatic hydrocarbons (PAH), alkanes and alkanols, while the lipid-soluble fraction would be enriched with fatty acids. The results from MTT assay showed that cytotoxicity of the PM(2.5) was much more severe than the PM(2.5-10), suggesting that toxic SEOC were confined to the fine particles. By and large, the DMSO solubles were much more toxic than the lipid solubles. The degree of cytotoxicity of the DMSO soluble samples is positively correlated to the amount of particulates present in the ambient air. For the PM(2.5), the winter samples were significantly more toxic than the summer samples in terms of cell killing, which seemed to be a direct reflection of the total loading of organic matter in the samples. Results from Comet assays showed that SEOC samples of PM(2.5) derived from winter months induced DNA damage at dosages resulting in no obvious cell killing in the MTT assay. Thus, long-term exposure to non-killing dosage of air pollutants may lead to the accumulation of DNA lesions, which may be one of the mechanisms responsible for the chronic adverse health effects of particulate air pollution.     

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.     

空气污染对信鸽比赛个体归巢速度的影响

空气污染作为一种有害的环境因素,对人类及动物的生理、心理均有影响.在鸟类中,信鸽(Columba livia)是研究空气污染影响的理想模型.为探究空气污染的行为学效应,通过收集并筛选2018和2019年成都市信鸽协会春秋两个季节举办的64场赛事共285羽参赛5场及以上的信鸽不同空距等级下的归巢速度,利用混合线性模型分析...     

Quantifying personal exposure to air pollution from smartphone‐based location data

Personal exposure assessment is a challenging task that requires both measurements of the state of the environment as well as the individual's movements. In this paper, we show how location data collected by smartphone applications can be exploited to quantify the personal exposure of a large group of people to air pollution. A Bayesian approach that blends air quality monitoring data with individual location data is proposed to assess the individual exposure over time, under uncertainty of both the pollutant level and the individual location. A comparison with personal exposure obtained assuming fixed locations for the individuals is also provided. Location data collected by the Earthquake Network research project are employed to quantify the dynamic personal exposure to fine particulate matter of around 2500 people living in Santiago (Chile) over a 4‐month period. For around 30% of individuals, the personal exposure based on people movements emerges significantly different over the static exposure. On the basis of this result and thanks to a simulation study, we claim that even when the individual location is known with nonnegligible error, this helps to better assess personal exposure to air pollution. The approach is flexible and can be adopted to quantify the personal exposure based on any location‐aware smartphone application.     

Control for confounding in the presence of measurement error in hierarchical models

Hierarchical modeling is becoming increasingly popular in epidemiology, particularly in air pollution studies. When potential confounding exists, a multilevel model yields better power to assess the independent effects of each predictor by gathering evidence across many sub-studies. If the predictors are measured with unknown error, bias can be expected in the individual substudies, and in the combined estimates of the second-stage model. We consider two alternative methods for estimating the independent effects of two predictors in a hierarchical model. We show both analytically and via simulation that one of these gives essentially unbiased estimates even in the presence of measurement error, at the price of a moderate reduction in power. The second avoids the potential for upward bias, at the price of a smaller reduction in power. Since measurement error is endemic in epidemiology, these approaches hold considerable potential. We illustrate the two methods by applying them to two air pollution studies. In the first, we re-analyze published data to show that the estimated effect of fine particles on daily deaths, independent of coarse particles, was downwardly biased by measurement error in the original analysis. The estimated effect of coarse particles becomes more protective using the new estimates. In the second example, we use published data on the association between airborne particles and daily deaths in 10 US cities to estimate the effect of gaseous air pollutants on daily deaths. The resulting effect size estimates were very small and the confidence intervals included zero.     

Composition of PM Affects Acute Vascular Inflammatory and Coagulative Markers - The RAPTES Project

Background

Exposure to ambient particulate matter (PM) has been associated with adverse cardiovascular effects in epidemiological studies. Current knowledge of independent effects of individual PM characteristics remains limited.

Methods

Using a semi-experimental design we investigated which PM characteristics were consistently associated with blood biomarkers believed to be predictive of the risk of cardiovascular events. We exposed healthy adult volunteers at 5 different locations chosen to provide PM exposure contrasts with reduced correlations among PM characteristics. Each of the 31 volunteers was exposed for 5 h, exercising intermittently, 3–7 times at different sites from March to October 2009. Extensive on-site exposure characterization included measurements of PM mass and number concentration, elemental- (EC) and organic carbon (OC), trace metals, sulfate, nitrate, and PM oxidative potential (OP). Before and 2 h and 18 h after exposure we measured acute vascular blood biomarkers - C-reactive protein, fibrinogen, platelet counts, von Willebrand Factor, and tissue plasminogen activator/plasminogen activator inhibitor-1 complex. We used two-pollutant models to assess which PM characteristics were most consistently associated with the measured biomarkers.

Results and Conclusion

We found OC, nitrate and sulfate to be most consistently associated with different biomarkers of acute cardiovascular risk. Associations with PM mass concentrations and OP were less consistent, whereas other measured components of the air pollution mixture, including PNC, EC, trace metals and NO₂, were not associated with the biomarkers after adjusting for other pollutants.     

Micronuclei induced by airborne particulate matter from Mexico City

Particulate air pollution is an important environmental health risk. In the present study, we have investigated the ability of chemically characterized water and organic-soluble extracts of PM(10) from two different regions of Mexico City to induce micronuclei in a human epithelial cell line. We also evaluated the association between the chemical characteristics of the PM and its genotoxicity. The airborne particulate samples were collected from an industrial and a residential region; a Hi-Vol air sampler was used to collect PM(10) on glass fiber filters. PM mass was determined by gravimetric analysis of the filters. One section of each PM(10) filter was agitated either with deionized water to extract water-soluble compounds or with dichloromethane to prepare organic-soluble compounds. The chemical composition of the extracts was determined by ion and gas chromatography and atomic adsorption spectroscopy. A549-human alveolar epithelial cells were exposed to different concentrations of PM(10) extracts and the cytokinesis blocked micronucleus assay was performed to measure DNA damage. Even though the industrial region had a higher PM concentration, higher amounts of metals and PAHs were found in the residential area. Both industrial and residential extracts induced a significant concentration-related increase in the micronuclei frequency. The PM(10) water-soluble industrial extract induced significantly more micronuclei than the one of the residential region; inversely, the organic residential extract induced more micronuclei than the one from the industrial region. The association between the induction of micronuclei and the chemical components obtained by the comparative analysis of standardized regression coefficients showed that cadmium and PAHs were significantly associated with micronuclei induction. Data indicate that water-soluble metals and the organic-soluble fraction of PM(10) are both important in the production of micronuclei. Effects observed, point to the risk of PM exposure and shows the need of integrative studies.     

Foraging trip duration of honeybee increases during a poor air quality episode and the increase persists thereafter

Increased concentration of airborne particulate matter (PM) in the atmosphere alters the degree of polarization of skylight which is used by honeybees for navigation during their foraging trips. However, little has empirically shown whether poor air quality indeed affects foraging performance (foraging trip duration) of honeybee. Here, we show apparent increases in the average duration of honeybee foraging during and after a heavy air pollution event compared with that of the pre‐event period. The average foraging duration of honeybees during the event increased by 32 min compared with the pre‐event conditions, indicating that 71% more time was spent on foraging. Moreover, the average foraging duration measured after the event did not recover to its pre‐event level. We further investigated whether an optical property (Depolarization Ratio, DR) of dominant PM in the atmosphere and level of air pollution (fine PM mass concentration) affect foraging trip duration. The result demonstrates the DR and fine PM mass concentration have significant effects on honeybee foraging trip duration. Foraging trip duration increases with decreasing DR while it increases with increasing fine PM mass concentration. In addition, the effects of fine PM mass concentration are synergistic with overcast skies. Our study implies that poor air quality could pose a new threat to bee foraging.     

Combining contingency tables with missing dimensions

We propose a methodology for estimating the cell probabilities in a multiway contingency table by combining partial information from a number of studies when not all of the variables are recorded in all studies. We jointly model the full set of categorical variables recorded in at least one of the studies, and we treat the variables that are not reported as missing dimensions of the study-specific contingency table. For example, we might be interested in combining several cohort studies in which the incidence in the exposed and nonexposed groups is not reported for all risk factors in all studies while the overall numbers of cases and cohort size is always available. To account for study-to-study variability, we adopt a Bayesian hierarchical model. At the first stage of the model, the observation stage, data are modeled by a multinomial distribution with fixed total number of observations. At the second stage, we use the logistic normal (LN) distribution to model variability in the study-specific cells' probabilities. Using this model and data augmentation techniques, we reconstruct the contingency table for each study regardless of which dimensions are missing, and we estimate population parameters of interest. Our hierarchical procedure borrows strength from all the studies and accounts for correlations among the cells' probabilities. The main difficulty in combining studies recording different variables is in maintaining a consistent interpretation of parameters across studies. The approach proposed here overcomes this difficulty and at the same time addresses the uncertainty arising from the missing dimensions. We apply our modeling strategy to analyze data on air pollution and mortality from 1987 to 1994 for six U.S. cities by combining six cross-classifications of low, medium, and high levels of mortality counts, particulate matter, ozone, and carbon monoxide with the complication that four of the six cities do not report all the air pollution variables. Our goals are to investigate the association between air pollution and mortality by reconstructing the tables with missing dimensions, to determine the most harmful pollutant combinations, and to make predictions about these key issues for a city other than the six sampled. We find that, for high levels of ozone and carbon monoxide, the number of cases with a high number of deaths increases as the levels of particulate matter, PM10, increases and that the most harmful combinations corresponds to high levels of PM10, confirming prior findings that levels of PM10 higher than the NAAQS standard are harmful.     

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.     

Ambient Air Pollution and the Progression of Atherosclerosis in Adults

Background

Cross-sectional studies suggest an association between exposure to ambient air pollution and atherosclerosis. We investigated the association between outdoor air quality and progression of subclinical atherosclerosis (common carotid artery intima-media thickness, CIMT).

Methodology/Principal Findings

We examined data from five double-blind randomized trials that assessed effects of various treatments on the change in CIMT. The trials were conducted in the Los Angeles area. Spatial models and land-use data were used to estimate the home outdoor mean concentration of particulate matter up to 2.5 micrometer in diameter (PM2.5), and to classify residence by proximity to traffic-related pollution (within 100 m of highways). PM2.5 and traffic proximity were positively associated with CIMT progression. Adjusted coefficients were larger than crude associations, not sensitive to modelling specifications, and statistically significant for highway proximity while of borderline significance for PM2.5 (P = 0.08). Annual CIMT progression among those living within 100 m of a highway was accelerated (5.5 micrometers/yr [95%CI: 0.13–10.79; p = 0.04]) or more than twice the population mean progression. For PM2.5, coefficients were positive as well, reaching statistical significance in the socially disadvantaged; in subjects reporting lipid lowering treatment at baseline; among participants receiving on-trial treatments; and among the pool of four out of the five trials.

Conclusion

Consistent with cross-sectional findings and animal studies, this is the first study to report an association between exposure to air pollution and the progression of atherosclerosis – indicated with CIMT change – in humans. Ostensibly, our results suggest that air pollution may contribute to the acceleration of cardiovascular disease development – the main causes of morbidity and mortality in many countries. However, the heterogeneity of the volunteering populations across the five trials, the limited sample size within trials and other relevant subgroups, and the fact that some key findings reached statistical significance in subgroups rather than the sample precludes generalizations to the general population.     

Daily hospital respiratory admissions and winter air mass types, Birmingham, UK

 A synoptic climatological approach is used to investigate linkages between air mass types (weather situations), the daily mean particulate matter with a size of 10 μm or less (PM10) concentrations and all respiratory hospital admissions for the Birmingham area, UK. Study results show distinct differential responses of respiratory admission rates to the six winter air mass types identified. Two of the three air masses associated with above average admission rates (continental anticyclonic gloom and continental anticyclonic fine and cold) also favour high PM10 levels. This association is suggestive of a possible linkage between weather, air quality and health. The remaining admissions-sensitive air mass type (cool moist maritime) does not favour high PM10 levels. This is considered to be indicative of a direct weather-health relationship. A sensitising mechanism is proposed to account for the linkages between air mass type, PM10 concentrations and respiratory response. Received: 4 August 1997 / Received after revision: 8 January 1999 / Accepted: 20 January 1999