Search tips
Search criteria

Results 1-18 (18)

Clipboard (0)

Select a Filter Below

more »
Year of Publication
Document Types
1.  Measurement error in two-stage analyses, with application to air pollution epidemiology 
Environmetrics  2014;24(8):501-517.
Public health researchers often estimate health effects of exposures (e.g., pollution, diet, lifestyle) that cannot be directly measured for study subjects. A common strategy in environmental epidemiology is to use a first-stage (exposure) model to estimate the exposure based on covariates and/or spatio-temporal proximity and to use predictions from the exposure model as the covariate of interest in the second-stage (health) model. This induces a complex form of measurement error. We propose an analytical framework and methodology that is robust to misspecification of the first-stage model and provides valid inference for the second-stage model parameter of interest.
We decompose the measurement error into components analogous to classical and Berkson error and characterize properties of the estimator in the second-stage model if the first-stage model predictions are plugged in without correction. Specifically, we derive conditions for compatibility between the first- and second-stage models that guarantee consistency (and have direct and important real-world design implications), and we derive an asymptotic estimate of finite-sample bias when the compatibility conditions are satisfied. We propose a methodology that (1) corrects for finite-sample bias and (2) correctly estimates standard errors. We demonstrate the utility of our methodology in simulations and an example from air pollution epidemiology.
PMCID: PMC3994141  PMID: 24764691
measurement error; spatial statistics; two-stage estimation; air pollution; environmental epidemiology
2.  A regionalized national universal kriging model using Partial Least Squares regression for estimating annual PM2.5 concentrations in epidemiology 
Many cohort studies in environmental epidemiology require accurate modeling and prediction of fine scale spatial variation in ambient air quality across the U.S. This modeling requires the use of small spatial scale geographic or “land use” regression covariates and some degree of spatial smoothing. Furthermore, the details of the prediction of air quality by land use regression and the spatial variation in ambient air quality not explained by this regression should be allowed to vary across the continent due to the large scale heterogeneity in topography, climate, and sources of air pollution. This paper introduces a regionalized national universal kriging model for annual average fine particulate matter (PM2.5) monitoring data across the U.S. To take full advantage of an extensive database of land use covariates we chose to use the method of Partial Least Squares, rather than variable selection, for the regression component of the model (the “universal” in “universal kriging”) with regression coefficients and residual variogram models allowed to vary across three regions defined as West Coast, Mountain West, and East. We demonstrate a very high level of cross-validated accuracy of prediction with an overall R2 of 0.88 and well-calibrated predictive intervals. In accord with the spatially varying characteristics of PM2.5 on a national scale and differing kriging smoothness parameters, the accuracy of the prediction varies by region with predictive intervals being notably wider in the West Coast and Mountain West in contrast to the East.
PMCID: PMC3763950  PMID: 24015108
Ambient air quality; Land use regression; National air quality model; Partial Least Squares; Particulate matter; Universal kriging
3.  Characterizing Spatial Patterns of Airborne Coarse Particulate (PM10–2.5) Mass and Chemical Components in Three Cities: The Multi-Ethnic Study of Atherosclerosis 
Environmental Health Perspectives  2014;122(8):823-830.
Background: The long-term health effects of coarse particular matter (PM10–2.5) are challenging to assess because of a limited understanding of the spatial variation in PM10–2.5 mass and its chemical components.
Objectives: We conducted a spatially intensive field study and developed spatial prediction models for PM10–2.5 mass and four selected species (copper, zinc, phosphorus, and silicon) in three American cities.
Methods: PM10–2.5 snapshot campaigns were conducted in Chicago, Illinois; St. Paul, Minnesota; and Winston-Salem, North Carolina, in 2009 for the Multi-Ethnic Study of Atherosclerosis and Coarse Airborne Particulate Matter (MESA Coarse). In each city, samples were collected simultaneously outside the homes of approximately 40 participants over 2 weeks in the winter and/or summer. City-specific and combined prediction models were developed using land use regression (LUR) and universal kriging (UK). Model performance was evaluated by cross-validation (CV).
Results: PM10–2.5 mass and species varied within and between cities in a manner that was predictable by geographic covariates. City-specific LUR models generally performed well for total mass (CV R2, 0.41–0.68), copper (CV R2, 0.51–0.86), phosphorus (CV R2, 0.50–0.76), silicon (CV R2, 0.48–0.93), and zinc (CV R2, 0.36–0.73). Models pooled across all cities inconsistently captured within-city variability. Little difference was observed between the performance of LUR and UK models in predicting concentrations.
Conclusions: Characterization of fine-scale spatial variability of these often heterogeneous pollutants using geographic covariates should reduce exposure misclassification and increase the power of epidemiological studies investigating the long-term health impacts of PM10–2.5.
Citation: Zhang K, Larson TV, Gassett A, Szpiro AA, Daviglus M, Burke GL, Kaufman JD, Adar SD. 2014. Characterizing spatial patterns of airborne coarse particulate (PM10–2.5) mass and chemical components in three cities: the Multi-Ethnic Study of Atherosclerosis. Environ Health Perspect 122:823–830;
PMCID: PMC4123025  PMID: 24642481
4.  Predicting Intra-Urban Variation in Air Pollution Concentrations with Complex Spatio-Temporal Dependencies 
Environmetrics  2009;21(6):606-631.
We describe a methodology for assigning individual estimates of long-term average air pollution concentrations that accounts for a complex spatio-temporal correlation structure and can accommodate spatio-temporally misaligned observations. This methodology has been developed as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the U.S. EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. Our hierarchical model decomposes the space-time field into a “mean” that includes dependence on covariates and spatially varying seasonal and long-term trends and a “residual” that accounts for spatially correlated deviations from the mean model. The model accommodates complex spatio-temporal patterns by characterizing the temporal trend at each location as a linear combination of empirically derived temporal basis functions, and embedding the spatial fields of coefficients for the basis functions in separate linear regression models with spatially correlated residuals (universal kriging). This approach allows us to implement a scalable single-stage estimation procedure that easily accommodates a significant number of missing observations at some monitoring locations. We apply the model to predict long-term average concentrations of oxides of nitrogen (NOx) from 2005–2007 in the Los Angeles area, based on data from 18 EPA Air Quality System regulatory monitors. The cross-validated R2 is 0.67. The MESA Air study is also collecting additional concentration data as part of a supplementary monitoring campaign. We describe the sampling plan and demonstrate in a simulation study that the additional data will contribute to improved predictions of long-term average concentrations.
PMCID: PMC4029437  PMID: 24860253
Air Pollution; Exposure Assessment; Hierarchical Modeling; Spatio-Temporal Modeling; Maximum Likelihood; Universal Kriging
5.  Exposure measurement error in PM2.5 health effects studies: A pooled analysis of eight personal exposure validation studies 
Environmental Health  2014;13:2.
Exposure measurement error is a concern in long-term PM2.5 health studies using ambient concentrations as exposures. We assessed error magnitude by estimating calibration coefficients as the association between personal PM2.5 exposures from validation studies and typically available surrogate exposures.
Daily personal and ambient PM2.5, and when available sulfate, measurements were compiled from nine cities, over 2 to 12 days. True exposure was defined as personal exposure to PM2.5 of ambient origin. Since PM2.5 of ambient origin could only be determined for five cities, personal exposure to total PM2.5 was also considered. Surrogate exposures were estimated as ambient PM2.5 at the nearest monitor or predicted outside subjects’ homes. We estimated calibration coefficients by regressing true on surrogate exposures in random effects models.
When monthly-averaged personal PM2.5 of ambient origin was used as the true exposure, calibration coefficients equaled 0.31 (95% CI:0.14, 0.47) for nearest monitor and 0.54 (95% CI:0.42, 0.65) for outdoor home predictions. Between-city heterogeneity was not found for outdoor home PM2.5 for either true exposure. Heterogeneity was significant for nearest monitor PM2.5, for both true exposures, but not after adjusting for city-average motor vehicle number for total personal PM2.5.
Calibration coefficients were <1, consistent with previously reported chronic health risks using nearest monitor exposures being under-estimated when ambient concentrations are the exposure of interest. Calibration coefficients were closer to 1 for outdoor home predictions, likely reflecting less spatial error. Further research is needed to determine how our findings can be incorporated in future health studies.
PMCID: PMC3922798  PMID: 24410940
Exposure measurement error; Fine particles; Fine particles of ambient origin; Monitoring data; Spatio-temporal models
6.  Vascular Responses to Long- and Short-Term Exposure to Fine Particulate Matter 
This study evaluated the association of long- and short-term air pollutant exposures with flow-mediated dilation (FMD) and baseline arterial diameter (BAD) of the brachial artery using ultrasound in a large multicity cohort.
Exposures to ambient air pollution, especially long-term exposure to particulate matter <2.5 μm in aerodynamic diameter (PM2.5), are linked with cardiovascular mortality. Short-term exposure to PM2.5 has been associated with decreased FMD and vasoconstriction, suggesting that adverse effects of PM2.5 may involve endothelial dysfunction. However, long-term effects of PM2.5 on endothelial dysfunction have not been investigated.
FMD and BAD were measured by brachial artery ultrasound at the initial examination of the Multi-Ethnic Study of Atherosclerosis. Long-term PM2.5 concentrations were estimated for the year 2000 at each participant’s residence (n = 3,040) using a spatio-temporal model informed by cohort-specific monitoring. Short-term PM2.5 concentrations were based on daily central-site monitoring in each of the 6 cities.
An interquartile increase in long-term PM2.5 concentration (3 μg/m3) was associated with a 0.3% decrease in FMD (95% confidence interval [CI] of difference: −0.6 to −0.03; p = 0.03), adjusting for demographic characteristics, traditional risk factors, sonographers, and 1/BAD. Women, nonsmokers, younger participants, and those with hypertension seemed to show a greater association of PM2.5 with FMD. FMD was not significantly associated with short-term variation in PM2.5 (−0.1% per 12 μg/m3 daily increase [95% CI: −0.2 to 0.04] on the day before examination).
Long-term PM2.5 exposure was significantly associated with decreased endothelial function according to brachial ultrasound results. These findings may elucidate an important pathway linking air pollution and cardiovascular mortality.
PMCID: PMC3665082  PMID: 23103035
air pollution; atherosclerosis; cardiovascular mortality; endothelial function; flow-mediated dilation; traffic
7.  Prospective Study of Particulate Air Pollution Exposures, Subclinical Atherosclerosis, and Clinical Cardiovascular Disease 
American Journal of Epidemiology  2012;176(9):825-837.
The Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) was initiated in 2004 to investigate the relation between individual-level estimates of long-term air pollution exposure and the progression of subclinical atherosclerosis and the incidence of cardiovascular disease (CVD). MESA Air builds on a multicenter, community-based US study of CVD, supplementing that study with additional participants, outcome measurements, and state-of-the-art air pollution exposure assessments of fine particulate matter, oxides of nitrogen, and black carbon. More than 7,000 participants aged 45–84 years are being followed for over 10 years for the identification and characterization of CVD events, including acute myocardial infarction and other coronary artery disease, stroke, peripheral artery disease, and congestive heart failure; cardiac procedures; and mortality. Subcohorts undergo baseline and follow-up measurements of coronary artery calcium using computed tomography and carotid artery intima-medial wall thickness using ultrasonography. This cohort provides vast exposure heterogeneity in ranges currently experienced and permitted in most developed nations, and the air monitoring and modeling methods employed will provide individual estimates of exposure that incorporate residence-specific infiltration characteristics and participant-specific time-activity patterns. The overarching study aim is to understand and reduce uncertainty in health effect estimation regarding long-term exposure to air pollution and CVD.
PMCID: PMC3571256  PMID: 23043127
air pollution; atherosclerosis; cardiovascular diseases; environmental exposure; epidemiologic methods; particulate matter
8.  A National Prediction Model for PM2.5 Component Exposures and Measurement Error–Corrected Health Effect Inference 
Environmental Health Perspectives  2013;121(9):1017-1025.
Background: Studies estimating health effects of long-term air pollution exposure often use a two-stage approach: building exposure models to assign individual-level exposures, which are then used in regression analyses. This requires accurate exposure modeling and careful treatment of exposure measurement error.
Objective: To illustrate the importance of accounting for exposure model characteristics in two-stage air pollution studies, we considered a case study based on data from the Multi-Ethnic Study of Atherosclerosis (MESA).
Methods: We built national spatial exposure models that used partial least squares and universal kriging to estimate annual average concentrations of four PM2.5 components: elemental carbon (EC), organic carbon (OC), silicon (Si), and sulfur (S). We predicted PM2.5 component exposures for the MESA cohort and estimated cross-sectional associations with carotid intima-media thickness (CIMT), adjusting for subject-specific covariates. We corrected for measurement error using recently developed methods that account for the spatial structure of predicted exposures.
Results: Our models performed well, with cross-validated R2 values ranging from 0.62 to 0.95. Naïve analyses that did not account for measurement error indicated statistically significant associations between CIMT and exposure to OC, Si, and S. EC and OC exhibited little spatial correlation, and the corrected inference was unchanged from the naïve analysis. The Si and S exposure surfaces displayed notable spatial correlation, resulting in corrected confidence intervals (CIs) that were 50% wider than the naïve CIs, but that were still statistically significant.
Conclusion: The impact of correcting for measurement error on health effect inference is concordant with the degree of spatial correlation in the exposure surfaces. Exposure model characteristics must be considered when performing two-stage air pollution epidemiologic analyses because naïve health effect inference may be inappropriate.
Citation: Bergen S, Sheppard L, Sampson PD, Kim SY, Richards M, Vedal S, Kaufman JD, Szpiro AA. 2013. A national prediction model for PM2.5 component exposures and measurement error–corrected health effect inference. Environ Health Perspect 121:1017–1025;
PMCID: PMC3764074  PMID: 23757600
9.  Association of Long-Term Air Pollution with Ventricular Conduction and Repolarization Abnormalities 
Epidemiology (Cambridge, Mass.)  2011;22(6):773-780.
Short-term exposure to air pollution may affect ventricular repolarization, but there is limited information on how long-term exposures might affect the surface ventricular electrocardiographic (ECG) abnormalities associated with cardiovascular events. We carried out a study to determine whether long-term air pollution exposure is associated with abnormalities of ventricular repolarization and conduction in adults without known cardiovascular disease.
A total of 4783 participants free of clinical cardiovascular disease in the Multi-Ethnic Study of Atherosclerosis underwent 12-lead ECG examinations, cardiac-computed tomography and calcium scoring, as well as estimation of air pollution exposure using a finely resolved spatio-temporal model to determine long-term average individual exposure to fine particulate matter (PM2.5) and proximity to major roadways. We assessed ventricular electrical abnormalities including presence of QT prolongation (Rautaharju QTrr criteria) and intraventricular conduction delay (QRS duration > 120 msec). We used logistic regression to determine the adjusted relationship between air pollution exposures and ECG abnormalities.
A 10 µg/m3-increase in estimated residential PM2.5 was associated with an increased odds of prevalent QT prolongation (adjusted odds ratio [OR]= 1.6 [95% confidence interval (CI)= 1.2 to 2.2]) and intraventricular conduction delay (OR 1.7, 95% CI: 1.0 to 2.6, independent of coronary-artery calcium score. Living near major roadways was not associated with ventricular electrical abnormalities. No significant evidence of effect modification by traditional risk factors or study site was observed.
This study demonstrates an association between long-term exposure to air pollution and ventricular repolarization and conduction abnormalities in adults without clinical cardiovascular disease, independent of subclinical coronary arterial calcification.
PMCID: PMC3197855  PMID: 21918454
10.  Efficient measurement error correction with spatially misaligned data 
Biostatistics (Oxford, England)  2011;12(4):610-623.
Association studies in environmental statistics often involve exposure and outcome data that are misaligned in space. A common strategy is to employ a spatial model such as universal kriging to predict exposures at locations with outcome data and then estimate a regression parameter of interest using the predicted exposures. This results in measurement error because the predicted exposures do not correspond exactly to the true values. We characterize the measurement error by decomposing it into Berkson-like and classical-like components. One correction approach is the parametric bootstrap, which is effective but computationally intensive since it requires solving a nonlinear optimization problem for the exposure model parameters in each bootstrap sample. We propose a less computationally intensive alternative termed the “parameter bootstrap” that only requires solving one nonlinear optimization problem, and we also compare bootstrap methods to other recently proposed methods. We illustrate our methodology in simulations and with publicly available data from the Environmental Protection Agency.
PMCID: PMC3169665  PMID: 21252080
Environmental epidemiology; Environmental statistics; Exposure modeling; Kriging; Measurement error
11.  Does More Accurate Exposure Prediction Necessarily Improve Health Effect Estimates? 
Epidemiology (Cambridge, Mass.)  2011;22(5):680-685.
A unique challenge in air pollution cohort studies and similar applications in environmental epidemiology is that exposure is not measured directly at subjects’ locations. Instead, pollution data from monitoring stations at some distance from the study subjects are used to predict exposures, and these predicted exposures are used to estimate the health effect parameter of interest. It is usually assumed that minimizing the error in predicting the true exposure will improve health effect estimation. We show in a simulation study that this is not always the case. We interpret our results in light of recently developed statistical theory for measurement error, and we discuss implications for the design and analysis of epidemiologic research.
PMCID: PMC3195520  PMID: 21716114
12.  Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) 
Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA.
The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a “snapshot” sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution.
LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R2 and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software.
UK models consistently performed as well as or better than the analogous LUR models. The best CV R2 values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R2 values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R2 values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively.
High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK.
PMCID: PMC3146303  PMID: 21808599
Universal kriging; land use regression; spatial modeling; air pollution; exposure assessment; Los Angeles
13.  Confounding and exposure measurement error in air pollution epidemiology 
Studies in air pollution epidemiology may suffer from some specific forms of confounding and exposure measurement error. This contribution discusses these, mostly in the framework of cohort studies. Evaluation of potential confounding is critical in studies of the health effects of air pollution. The association between long-term exposure to ambient air pollution and mortality has been investigated using cohort studies in which subjects are followed over time with respect to their vital status. In such studies, control for individual-level confounders such as smoking is important, as is control for area-level confounders such as neighborhood socio-economic status. In addition, there may be spatial dependencies in the survival data that need to be addressed. These issues are illustrated using the American Cancer Society Cancer Prevention II cohort. Exposure measurement error is a challenge in epidemiology because inference about health effects can be incorrect when the measured or predicted exposure used in the analysis is different from the underlying true exposure. Air pollution epidemiology rarely if ever uses personal measurements of exposure for reasons of cost and feasibility. Exposure measurement error in air pollution epidemiology comes in various dominant forms, which are different for time-series and cohort studies. The challenges are reviewed and a number of suggested solutions are discussed for both study domains.
PMCID: PMC3353104  PMID: 22662023
Air pollution; Epidemiology; Confounding; Measurement error
14.  Air Pollution and the Microvasculature: A Cross-Sectional Assessment of In Vivo Retinal Images in the Population-Based Multi-Ethnic Study of Atherosclerosis (MESA) 
PLoS Medicine  2010;7(11):e1000372.
Sara Adar and colleagues show that residing in locations with higher air pollution concentrations and experiencing daily increases in air pollution are associated with narrower retinal arteriolar diameters in older individuals, thus providing a link between air pollution and cardiovascular disease.
Long- and short-term exposures to air pollution, especially fine particulate matter (PM2.5), have been linked to cardiovascular morbidity and mortality. One hypothesized mechanism for these associations involves microvascular effects. Retinal photography provides a novel, in vivo approach to examine the association of air pollution with changes in the human microvasculature.
Methods and Findings
Chronic and acute associations between residential air pollution concentrations and retinal vessel diameters, expressed as central retinal arteriolar equivalents (CRAE) and central retinal venular equivalents (CRVE), were examined using digital retinal images taken in Multi-Ethnic Study of Atherosclerosis (MESA) participants between 2002 and 2003. Study participants (46 to 87 years of age) were without clinical cardiovascular disease at the baseline examination (2000–2002). Long-term outdoor concentrations of PM2.5 were estimated at each participant's home for the 2 years preceding the clinical exam using a spatio-temporal model. Short-term concentrations were assigned using outdoor measurements on the day preceding the clinical exam. Residential proximity to roadways was also used as an indicator of long-term traffic exposures. All associations were examined using linear regression models adjusted for subject-specific age, sex, race/ethnicity, education, income, smoking status, alcohol use, physical activity, body mass index, family history of cardiovascular disease, diabetes status, serum cholesterol, glucose, blood pressure, emphysema, C-reactive protein, medication use, and fellow vessel diameter. Short-term associations were further controlled for weather and seasonality. Among the 4,607 participants with complete data, CRAE were found to be narrower among persons residing in regions with increased long- and short-term levels of PM2.5. These relationships were observed in a joint exposure model with −0.8 µm (95% confidence interval [CI] −1.1 to −0.5) and −0.4 µm (95% CI −0.8 to 0.1) decreases in CRAE per interquartile increases in long- (3 µg/m3) and short-term (9 µg/m3) PM2.5 levels, respectively. These reductions in CRAE are equivalent to 7- and 3-year increases in age in the same cohort. Similarly, living near a major road was also associated with a −0.7 µm decrease (95% CI −1.4 to 0.1) in CRAE. Although the chronic association with CRAE was largely influenced by differences in exposure between cities, this relationship was generally robust to control for city-level covariates and no significant differences were observed between cities. Wider CRVE were associated with living in areas of higher PM2.5 concentrations, but these findings were less robust and not supported by the presence of consistent acute associations with PM2.5.
Residing in regions with higher air pollution concentrations and experiencing daily increases in air pollution were each associated with narrower retinal arteriolar diameters in older individuals. These findings support the hypothesis that important vascular phenomena are associated with small increases in short-term or long-term air pollution exposures, even at current exposure levels, and further corroborate reported associations between air pollution and the development and exacerbation of clinical cardiovascular disease.
Please see later in the article for the Editors' Summary
Editors' Summary
Cardiovascular disease (CVD)—disease that affects the heart and/or the blood vessels—is a common cause of illness and death among adults in developed countries. In the United States, for example, the leading cause of death is coronary heart disease, a CVD in which narrowing of the heart's arteries by atherosclerotic plaques (fatty deposits that build up with age) slows the blood supply to the heart and may eventually cause a heart attack (myocardial infarction). Other types of CVD include stroke (in which atherosclerotic plaques interrupt the brain's blood supply) and peripheral arterial disease (in which the blood supply to the limbs is blocked). Smoking, high blood pressure, high blood levels of cholesterol (a type of fat), having diabetes, being overweight, and being physically inactive all increase a person's risk of developing CVD. Treatments for CVD include lifestyle changes and taking drugs that lower blood pressure or blood cholesterol levels.
Why Was This Study Done?
Another risk factor for CVD is exposure to long-term and/or short-term air pollution. Fine particle pollution or PM2.5 is particularly strongly associated with an increased risk of CVD. PM2.5—particulate matter 2.5 µm in diameter or 1/30th the diameter of a human hair—is mainly produced by motor vehicles, power plants, and other combustion sources. Why PM2.5 increases CVD risk is not clear but one possibility is that it alters the body's microvasculature (fine blood vessels known as capillaries, arterioles, and venules), thereby impairing the blood flow through the heart and brain. In this study, the researchers use noninvasive digital retinal photography to investigate whether there is an association between air pollution and changes in the human microvasculature. The retina—a light-sensitive layer at the back of the eye that converts images into electrical messages and sends them to the brain—has a dense microvasculature. Retinal photography is used to check the retinal microvasculature for signs of potentially blinding eye diseases such as diabetic retinopathy. Previous studies have found that narrower than normal retinal arterioles and wider than normal retinal venules are associated with CVD.
What Did the Researchers Do and Find?
The researchers used digital retinal photography to measure the diameters of retinal blood vessels in the participants of the Multi-Ethnic Study of Atherosclerosis (MESA). This study is investigating CVD progression in people aged 45–84 years of various ethnic backgrounds who had no CVD symptoms when they enrolled in the study in 2000–2002. The researchers modeled the long-term outdoor concentration of PM2.5 at each participant's house for the 2-year period preceding the retinal examination (which was done between 2002 and 2003) using data on PM2.5 levels collected by regulatory monitoring stations as well as study-specific air samples collected outside of the homes and in the communities of study participants. Outdoor PM2.5 measurements taken the day before the examination provided short-term PM2.5 levels. Among the 4,607 MESA participants who had complete data, retinal arteriolar diameters were narrowed among those who lived in regions with increased long- and short-term PM2.5 levels. Specifically, an increase in long-term PM2.5 concentrations of 3 µg/m3 was associated with a 0.8 µm decrease in arteriolar diameter, a reduction equivalent to that seen for a 7-year increase in age in this group of people. Living near a major road, another indicator of long-term exposure to PM2.5 pollution, was also associated with narrowed arterioles. Finally, increased retinal venular diameters were weakly associated with long-term high PM2.5 concentrations.
What Do These Findings Mean?
These findings indicate that living in areas with long-term air pollution or being exposed to short-term air pollution is associated with narrowing of the retinal arterioles in older individuals. They also show that widening of retinal venules is associated with long-term (but not short-term) PM2.5 pollution. Together, these findings support the hypothesis that long- and short-term air pollution increases CVD risk through effects on the microvasculature. However, they do not prove that PM2.5 is the constituent of air pollution that drives microvascular changes—these findings could reflect the toxicity of another pollutant or the pollution mixture as a whole. Importantly, these findings show that microvascular changes can occur at the PM2.5 levels that commonly occur in developed countries, which are well below those seen in developing countries. Worryingly, they also suggest that the deleterious cardiovascular effects of air pollution could occur at levels below existing regulatory standards.
Additional Information
Please access these Web sites via the online version of this summary at 10.1371/journal.pmed.1000372.
The American Heart Association provides information for patients and caregivers on all aspects of cardiovascular disease (in several languages), including information on air pollution, heart disease, and stroke
The US Centers for Disease Control and Prevention has information on heart disease and on stroke
Information is available from the British Heart Foundation on cardiovascular disease
The UK National Health Service Choices website provides information for patients and caregivers about cardiovascular disease
MedlinePlus provides links to other sources of information on heart disease and on vascular disease (in English and Spanish)
The AIRNow site provides information about US air quality and about air pollution and health
The Air Quality Archive has up-to-date information about air pollution in the UK and information about the health effects of air pollution
The US Environmental Protection Agency has information on PM2.5
The following Web sites contain information available on the MESA and MESA Air studies
PMCID: PMC2994677  PMID: 21152417
15.  Common Genetic Variation, Residential Proximity to Traffic Exposure, and Left Ventricular Mass: The Multi-Ethnic Study of Atherosclerosis 
Environmental Health Perspectives  2010;118(7):962-969.
Elevated left ventricular mass (LVM) is a strong predictor of negative cardiovascular outcomes, including heart failure, stroke, and sudden cardiac death. A relationship between close (< 50 m compared with > 150 m) residential proximity to major roadways and higher LVM has previously been described, but the mechanistic pathways that are involved in this relationship are not known. Understanding genetic factors that influence susceptibility to these effects may provide insight into relevant mechanistic pathways.
We set out to determine whether genetic polymorphisms in genes affecting vascular and autonomic function, blood pressure, or inflammation influence the relationship between traffic proximity and LVM.
This was a cross-sectional study of 1,376 genotyped participants in the Multi-Ethnic Study of Atherosclerosis, with cardiac magnetic resonance imaging performed between 2000 and 2002. The impact of tagged single-nucleotide polymorphisms (tagSNPs) and inferred haplotypes in 12 candidate genes (ACE, ADRB2, AGT, AGTR1, ALOX15, EDN1, GRK4, PTGS1, PTGS2, TLR4, VEGFA, and VEGFB) on the relationship between residential proximity to major roadways and LVM was analyzed using multiple linear regression, adjusting for multiple potential confounders.
After accounting for multiple testing and comparing homozygotes, tagSNPs in the type 1 angiotensin II receptor (AGTR1, rs6801836) and arachidonate 15-lipoxygenase (ALOX15, rs2664593) genes were each significantly (q < 0.2) associated with a 9–10% difference in the association between residential proximity to major roadways and LVM. Participants with suboptimal blood pressure control demonstrated stronger interactions between AGTR1 and traffic proximity.
Common polymorphisms in genes responsible for vascular function, inflammation, and oxidative stress appear to modify associations between proximity to major roadways and LVM. Further understanding of how genes modify effects of air pollution on CVD may help guide research efforts into specific mechanistic pathways.
PMCID: PMC2920916  PMID: 20308035
AGTR1; ALOX15; cardiac structure; cardiac MRI; gene-environment interactions; left ventricular mass; traffic, air pollution
16.  Exposure to Traffic and Left Ventricular Mass and Function 
Rationale: Ambient air pollution has been associated with heart failure morbidity and mortality. The mechanisms responsible for these associations are unknown but may include the effects of traffic-related pollutants on vascular or autonomic function.
Objectives: We assessed the cross-sectional relation between long-term air pollution, traffic exposures, and important end-organ measures of alterations in cardiac function—left ventricular mass index (LVMI) and ejection fraction—in the Multi-Ethnic Study of Atherosclerosis, a multicenter study of adults without previous clinical cardiovascular disease.
Methods: A total of 3,827 eligible participants (aged 45–84 yr) underwent cardiac magnetic resonance imaging between 2000 and 2002. We estimated air pollution exposures using residential proximity to major roadways and interpolated concentrations of fine particulate matter (less than 2.5 microns in diameter). We examined adjusted associations between these exposures and left ventricular mass and function.
Measurements and Main Results: Relative to participants living more than 150 m from a major roadway, participants living within 50 m of a major roadway showed an adjusted 1.4 g/m2 (95% CI, 0.3–2.5) higher LVMI, a difference in mass corresponding to a 5.6 mm Hg greater systolic blood pressure. Ejection fraction was not associated with proximity to major roadways. Limited variability in estimates of fine particulate matter was observed within cities, and no associations with particulate matter were found for either outcome after adjustment for center.
Conclusions: Living in close proximity to major roadways is associated with higher LVMI, suggesting chronic vascular end-organ damage from a traffic-related environmental exposure. Air pollutants or another component of roadway proximity, such as noise, could be responsible.
PMCID: PMC2675567  PMID: 19164703
epidemiology; particulate matter; hypertrophy; heart failure; magnetic resonance imaging
17.  Predicting Airborne Particle Levels Aboard Washington State School Buses 
School buses contribute substantially to childhood air pollution exposures yet they are rarely quantified in epidemiology studies. This paper characterizes fine particulate matter (PM2.5) aboard school buses as part of a larger study examining the respiratory health impacts of emission-reducing retrofits.
To assess onboard concentrations, continuous PM2.5 data were collected during 85 trips aboard 43 school buses during normal driving routines, and aboard hybrid lead vehicles traveling in front of the monitored buses during 46 trips. Ordinary and partial least square regression models for PM2.5 onboard buses were created with and without control for roadway concentrations, which were also modeled. Predictors examined included ambient PM2.5 levels, ambient weather, and bus and route characteristics.
Concentrations aboard school buses (21 μg/m3) were four and two-times higher than ambient and roadway levels, respectively. Differences in PM2.5 levels between the buses and lead vehicles indicated an average of 7 μg/m3 originating from the bus's own emission sources. While roadway concentrations were dominated by ambient PM2.5, bus concentrations were influenced by bus age, diesel oxidative catalysts, and roadway concentrations. Cross validation confirmed the roadway models but the bus models were less robust.
These results confirm that children are exposed to air pollution from the bus and other roadway traffic while riding school buses. In-cabin air pollution is higher than roadway concentrations and is likely influenced by bus characteristics.
PMCID: PMC2491491  PMID: 18985175
Air pollution; diesel; school buses; particulate matter; traffic
18.  Validating National Kriging Exposure Estimation 
PMCID: PMC1913586  PMID: 17637891

Results 1-18 (18)