In 2005 through 2008 a small rural mountain valley community engaged in a wood stove changeout program to address concerns of poor ambient air quality. During this program we assessed changes to indoor air quality before and after the introduction of a new, lower emission wood stove. We previously reported a greater than 70% reduction in indoor PM2.5 concentrations in homes following the installation of a new EPA-certified stove within the home. We report here on follow-up of the experiences in these and other homes over three winters of sample collection. In 21 homes, we compared pre-changeout PM2.5 concentrations (mean (sd) = 45.0 (33.0) μg/m3) to multiple post-changeout measures of PM2.5 concentrations using a DustTrak. The mean reduction (and 95% confidence interval) from pre-changeout to post-changeout was −18.5 μg/m3 (−31.9, −5.2), adjusting for ambient PM2.5, ambient temperature, and other factors. Findings across homes and across years were highly variable, and a subset of homes did not experience a reduction in PM2.5 following changeout. Reductions were also observed for organic carbon, elemental carbon, and levoglucosan, but increases were observed for dehydroabietic acid and abietic acid. Despite overall improvements in indoor air quality, the varied response across homes may be due to factors other than the introduction of a new wood stove.
Intervention; wood smoke; wood stove; biomass; particulate matter; Libby; Montana
Background: Although research has shown that low socioeconomic status (SES) and minority communities have higher exposure to air pollution, few studies have simultaneously investigated the associations of individual and neighborhood SES with pollutants across multiple sites.
Objectives: We characterized the distribution of ambient air pollution by both individual and neighborhood SES using spatial regression methods.
Methods: The study population comprised 6,140 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). Year 2000 annual average ambient PM2.5 and NOx concentrations were calculated for each study participant’s home address at baseline examination. We investigated individual and neighborhood (2000 U.S. Census tract level) SES measures corresponding to the domains of income, wealth, education, and occupation. We used a spatial intrinsic conditional autoregressive model for multivariable analysis and examined pooled and metropolitan area–specific models.
Results: A 1-unit increase in the z-score for family income was associated with 0.03-μg/m3 lower PM2.5 (95% CI: –0.05, –0.01) and 0.93% lower NOx (95% CI: –1.33, –0.53) after adjustment for covariates. A 1-SD–unit increase in the neighborhood’s percentage of persons with at least a high school degree was associated with 0.47-μg/m3 lower mean PM2.5 (95% CI: –0.55, –0.40) and 9.61% lower NOx (95% CI: –10.85, –8.37). Metropolitan area–specific results exhibited considerable heterogeneity. For example, in New York, high-SES neighborhoods were associated with higher concentrations of pollution.
Conclusions: We found statistically significant associations of SES measures with predicted air pollutant concentrations, demonstrating the importance of accounting for neighborhood- and individual-level SES in air pollution health effects research.
Citation: Hajat A, Diez-Roux AV, Adar SD, Auchincloss AH, Lovasi GS, O’Neill MS, Sheppard L, Kaufman JD. 2013. Air pollution and individual and neighborhood socioeconomic status: evidence from the Multi-Ethnic Study of Atherosclerosis (MESA). Environ Health Perspect 121:1325–1333; http://dx.doi.org/10.1289/ehp.1206337
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.
air pollution; atherosclerosis; cardiovascular diseases; environmental exposure; epidemiologic methods; particulate matter
Manganese (Mn), an established neurotoxicant, is a common component of welding fume. The neurological phenotype associated with welding exposures has not been well described. Prior epidemiologic evidence linking occupational welding to parkinsonism is mixed, and remains controversial.
This was a cross-sectional and nested case–control study to investigate the prevalence and phenotype of parkinsonism among 811 shipyard and fabrication welders recruited from trade unions. Two reference groups included 59 non-welder trade workers and 118 newly diagnosed, untreated idiopathic PD patients. Study subjects were examined by a movement disorders specialist using the Unified Parkinson Disease Rating Scale motor subsection 3 (UPDRS3). Parkinsonism cases were defined as welders with UPDRS3 score ≥15. Normal was defined as UPDRS3 < 6. Exposure was classified as intensity adjusted, cumulative years of welding. Adjusted prevalence ratios for parkinsonism were calculated in relation to quartiles of welding years.
The overall prevalence estimate of parkinsonism was 15.6% in welding exposed workers compared to 0% in the reference group. Among welders, we observed a U-shaped dose–response relation between weighted welding exposure-years and parkinsonism. UPDRS3 scores for most domains were similar between welders and newly diagnosed idiopathic Parkinson disease (PD) patients, except for greater frequency of rest tremor and asymmetry in PD patients.
This work-site based study among welders demonstrates a high prevalence of parkinsonism compared to nonwelding-exposed workers and a clinical phenotype that overlaps substantially with PD.
Parkinsonism; Parkinson disease; Welding; Manganese; Occupational exposures
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; http://dx.doi.org/10.1289/ehp.1206010
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.
Environmental epidemiology; Environmental statistics; Exposure modeling; Kriging; Measurement error
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.
Background: In air pollution time-series studies, the temporal pattern of the association of fine particulate matter (PM2.5; particulate matter ≤ 2.5 µm in aerodynamic diameter) and health end points has been observed to vary by disease category. The lag pattern of PM2.5 chemical constituents has not been well investigated, largely because daily data have not been available.
Objectives: We explored the lag structure for hospital admissions using daily PM2.5 chemical constituent data for 5 years in the Denver Aerosol Sources and Health (DASH) study.
Methods: We measured PM2.5 constituents, including elemental carbon, organic carbon, sulfate, and nitrate, at a central residential site from 2003 through 2007 and linked these daily pollution data to daily hospital admission counts in the five-county Denver metropolitan area. Total hospital admissions and subcategories of respiratory and cardiovascular admissions were examined. We assessed the lag structure of relative risks (RRs) of hospital admissions for PM2.5 and four constituents on the same day and from 1 to 14 previous days from a constrained distributed lag model; we adjusted for temperature, humidity, longer-term temporal trends, and day of week using a generalized additive model.
Results: RRs were generally larger at shorter lags for total cardiovascular admissions but at longer lags for total respiratory admissions. The delayed lag pattern was particularly prominent for asthma. Elemental and organic carbon generally showed more immediate patterns, whereas sulfate and nitrate showed delayed patterns.
Conclusion: In general, PM2.5 chemical constituents were found to have more immediate estimated effects on cardiovascular diseases and more delayed estimated effects on respiratory diseases, depending somewhat on the constituent.
air pollution; cardiovascular disease; chemical constituent; hospital admission; particulate matter; respiratory disease; time-series study
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.
Universal kriging; land use regression; spatial modeling; air pollution; exposure assessment; Los Angeles
Background: Epidemiologic studies of fine particulate matter [aerodynamic diameter ≤ 2.5 μm (PM2.5)] typically use outdoor concentrations as exposure surrogates. Failure to account for variation in residential infiltration efficiencies (Finf) will affect epidemiologic study results.
Objective: We aimed to develop models to predict Finf for > 6,000 homes in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study of PM2.5 exposure, subclinical cardiovascular disease, and clinical outcomes.
Methods: We collected 526 two-week, paired indoor–outdoor PM2.5 filter samples from a subset of study homes. PM2.5 elemental composition was measured by X-ray fluorescence, and Finf was estimated as the indoor/outdoor sulfur ratio. We regressed Finf on meteorologic variables and questionnaire-based predictors in season-specific models. Models were evaluated using the R2 and root mean square error (RMSE) from a 10-fold cross-validation.
Results: The mean ± SD Finf across all communities and seasons was 0.62 ± 0.21, and community-specific means ranged from 0.47 ± 0.15 in Winston-Salem, North Carolina, to 0.82 ± 0.14 in New York, New York. Finf was generally greater during the warm (> 18°C) season. Central air conditioning (AC) use, frequency of AC use, and window opening frequency were the most important predictors during the warm season; outdoor temperature and forced-air heat were the best cold-season predictors. The models predicted 60% of the variance in 2-week Finf, with an RMSE of 0.13.
Conclusions: We developed intuitive models that can predict Finf using easily obtained variables. Using these models, MESA Air will be the first large epidemiologic study to incorporate variation in residential Finf into an exposure assessment.
air exchange; attenuation; deposition; exposure misclassification; penetration; ventilation
The initiation and acceleration of atherosclerosis is hypothesized as a physiologic mechanism underlying associations between air pollution and cardiovascular effects. Despite toxicologic evidence, epidemiologic data are limited.
In this cross-sectional analysis we investigated exposure to fine particulate matter (PM2.5) and residential proximity to major roads in relation to abdominal aortic calcification a sensitive indicator of systemic atherosclerosis. Aortic calcification was measured by computed tomography among 1147 persons, in 5 U.S. metropolitan areas, enrolled in the Multi-Ethnic Study of Atherosclerosis (MESA). The presence and quantity of aortic calcification were modeled using relative risk regression and linear regression, respectively, with adjustment for potential confounders.
We observed a slightly elevated risk of aortic calcification (RR = 1.06; 95% confidence interval = 0.96–1.16) with a 10-μg/m3 contrast in PM2.5. The PM2.5-associated risk of aortic calcification was stronger among participants with long-term residence near a PM2.5 monitor (RR = 1.11; 1.00–1.24) and among participants not recently employed outside the home (RR = 1.10; 1.00–1.22). PM2.5 was not associated with an increase in the quantity of aortic calcification (Agatston score) and no roadway proximity effects were noted. There was indication of PM2.5 effect modification by lipid-lowering medication use, with greater effects among users, and PM2.5 associations were observed most consistently among Hispanics.
Although we did not find persuasive associations across our full study population, associations were stronger among participants with less exposure misclassification. These findings support the hypothesis of a relationship between particulate air pollution and systemic atherosclerosis.
Preterm delivery and preeclampsia are common adverse pregnancy outcomes that have been inconsistently associated with ambient air pollutant exposures.
We aimed to prospectively examine relations between exposures to ambient carbon monoxide (CO) and fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM2.5)] and risks of preeclampsia and preterm delivery.
We used data from 3,509 western Washington women who delivered infants between 1996 and 2006. We predicted ambient CO and PM2.5 exposures using regression models based on regional air pollutant monitoring data. Models contained predictor terms for year, month, weather, and land use characteristics. We evaluated several exposure windows, including prepregnancy, early pregnancy, the first two trimesters, the last month, and the last 3 months of pregnancy. Outcomes were identified using abstracted maternal medical record data. Covariate information was obtained from maternal interviews.
Predicted periconceptional CO exposure was significantly associated with preeclampsia after adjustment for maternal characteristics and season of conception [adjusted odds ratio (OR) per 0.1 ppm = 1.07; 95% confidence interval (CI), 1.02–1.13]. However, further adjustment for year of conception essentially nullified the association (adjusted OR = 0.98; 95% CI, 0.91–1.06). Associations between PM2.5 and preeclampsia were nonsignificant and weaker than associations estimated for CO, and neither air pollutant was strongly associated with preterm delivery. Patterns were similar across all exposure windows.
Because both CO concentrations and preeclampsia incidence declined during the study period, secular changes in another preeclampsia risk factor may explain the association observed here. We saw little evidence of other associations with preeclampsia or preterm delivery in this setting.
air pollution; carbon monoxide; fine particulate matter; preeclampsia; pregnancy preterm delivery
Exposure to carbon monoxide (CO) and other ambient air pollutants is associated with adverse pregnancy outcomes. While there are several methods of estimating CO exposure, few have been evaluated against exposure biomarkers. The authors examined the relation between estimated CO exposure and blood carboxyhemoglobin concentration in 708 pregnant western Washington State women (1996–2004). Carboxyhemoglobin was measured in whole blood drawn around 13 weeks’ gestation. CO exposure during the month of blood draw was estimated using a regression model containing predictor terms for year, month, street and population densities, and distance to the nearest major road. Year and month were the strongest predictors. Carboxyhemoglobin level was correlated with estimated CO exposure (ρ = 0.22, 95% confidence interval (CI): 0.15, 0.29). After adjustment for covariates, each 10% increase in estimated exposure was associated with a 1.12% increase in median carboxyhemoglobin level (95% CI: 0.54, 1.69). This association remained after exclusion of 286 women who reported smoking or being exposed to secondhand smoke (ρ = 0.24). In this subgroup, the median carboxyhemoglobin concentration increased 1.29% (95% CI: 0.67, 1.91) for each 10% increase in CO exposure. Monthly estimated CO exposure was moderately correlated with an exposure biomarker. These results support the validity of this regression model for estimating ambient CO exposures in this population and geographic setting.
air pollutants; carbon monoxide; carboxyhemoglobin; pregnancy
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.
Air pollution; Epidemiology; Confounding; Measurement error
Objectives: Although noise-induced hearing loss is completely preventable, it remains highly prevalent among construction workers. Hearing protection devices (HPDs) are commonly relied upon for exposure reduction in construction, but their use is complicated by intermittent and highly variable noise, inadequate industry support for hearing conservation, and lax regulatory enforcement.
Methods: As part of an intervention study designed to promote HPD use in the construction industry, we enrolled a cohort of 268 construction workers from a variety of trades at eight sites and evaluated their use of HPDs at baseline. We measured HPD use with two instruments, a questionnaire survey and a validated combination of activity logs with simultaneous dosimetry measurements. With these measurements, we evaluated potential predictors of HPD use based on components of Pender's revised health promotion model (HPM) and safety climate factors.
Results: Observed full-shift equivalent noise levels were above recommended limits, with a mean of 89.8 ± 4.9 dBA, and workers spent an average of 32.4 ± 18.6% of time in each shift above 85 dBA. We observed a bimodal distribution of HPD use from the activity card/dosimetry measures, with nearly 80% of workers reporting either almost never or almost always using HPDs. Fair agreement (kappa = 0.38) was found between the survey and activity card/dosimetry HPD use measures. Logistic regression models identified site, trade, education level, years in construction, percent of shift in high noise, and five HPM components as important predictors of HPD use at the individual level. Site safety climate factors were also predictors at the group level.
Conclusions: Full-shift equivalent noise levels on the construction sites assessed were well above the level at which HPDs are required, but usage rates were quite low. Understanding and predicting HPD use differs by methods used to assess use (survey versus activity card/dosimetry). Site, trade, and the belief that wearing HPD is not time consuming were the only predictors of HPD use common to both measures on an individual level. At the group level, perceived support for site safety and HPD use proved to be predictive of HPD use.
construction; hearing protectors; noise exposure
Most published epidemiology studies of long-term air pollution health effects have relied on central site monitoring to investigate regional-scale differences in exposure. Few cohort studies have had sufficient data to characterize localized variations in pollution, despite the fact that large gradients can exist over small spatial scales. Similarly, previous data have generally been limited to measurements of particle mass or several of the criteria gases. The Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) is an innovative investigation undertaken to link subclinical and clinical cardiovascular health effects with individual-level estimates of personal exposure to ambient-origin pollution. This project improves on prior work by implementing an extensive exposure assessment program to characterize long-term average concentrations of ambient-generated PM2.5, specific PM2.5 chemical components, and co-pollutants, with particular emphasis on capturing concentration gradients within cities.
This paper describes exposure assessment in MESA Air, including questionnaires, community sampling, home monitoring, and personal sampling. Summary statistics describing the performance of the sampling methods are presented along with descriptive statistics of the air pollution concentrations by city.
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.
AGTR1; ALOX15; cardiac structure; cardiac MRI; gene-environment interactions; left ventricular mass; traffic, air pollution
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.
epidemiology; particulate matter; hypertrophy; heart failure; magnetic resonance imaging
Ambient particulate matter (PM) is associated with cardiovascular morbidity and mortality. It has been proposed that PM induces a pro-thrombotic process, increasing the risk of cardiovascular events, with some support from epidemiological and laboratory-based models. Diesel exhaust is a major contributor to urban PM, and we conducted a controlled human exposure of diesel exhaust in healthy subjects.
To evaluate diesel exhaust exposure effects on fibrinolytic burden (D-dimer), platelet number, and endothelial injury (von Willebrand’s factor, VWF), inhibition of the fibrinolytic pathway (plasminogen activator inhibitor-1 [PAI-1]), and inflammation (C-reactive protein, CRP).
Materials and Methods
Randomized, crossover, double-blinded design, with 13 healthy participants exposed on three different days (≥ 2 weeks washout) to diesel exhaust at 0 (filtered air), 100μg PM2.5/m3 and 200μg PM2.5/m3. We assessed diesel exhaust-associated changes in D-dimer, VWF, PAI-1 and platelets at 3, 6 and 22 hours, and CRP at 22 hours, after exposure initiation.
Significant changes did not occur in any primary endpoints. Among secondary endpoints, diesel exhaust (200μg PM2.5/m3) effect on PAI-1 levels at 22 hours was of borderline significance, with a 1.32-fold decrease after exposure to diesel exhaust (200μg PM2.5/m3), relative to filtered air (CI 1.00 to 1.54). Diurnal patterns in D-dimer and PAI-1 were observed.
In healthy individuals, exposure to 200μg PM2.5/m3 diesel exhaust did not affect primary pro-thrombotic endpoints. Thus, these data do not support a diesel exhaust-induced pro-thrombotic phenomenon. Replication of these studies should be carried out to ascertain whether or not they inform our mechanistic understanding of air pollution’s cardiovascular effects.
diesel; coagulation; fibrinolysis; human; inhalation exposure; vehicle emissions
Disability associated with work-related musculoskeletal disorders is an increasingly serious societal problem. Although most injured workers return quickly to work, a substantial number do not. The costs of chronic disability to the injured worker, his or her family, employers, and society are enormous. A means of accurate early identification of injured workers at risk for chronic disability could enable these individuals to be targeted for early intervention to promote return to work and normal functioning. The purpose of this study is to develop statistical models that accurately predict chronic work disability from data obtained from administrative databases and worker interviews soon after a work injury. Based on these models, we will develop a brief instrument that could be administered in medical or workers' compensation settings to screen injured workers for chronic disability risk.
This is a population-based, prospective study. The study population consists of workers who file claims for work-related back injuries or carpal tunnel syndrome (CTS) in Washington State. The Washington State Department of Labor and Industries claims database is reviewed weekly to identify workers with new claims for work-related back injuries and CTS, and these workers are telephoned and invited to participate. Workers who enroll complete a computer-assisted telephone interview at baseline and one year later. The baseline interview assesses sociodemographic, employment-related, biomedical/health care, legal, and psychosocial risk factors. The follow-up interview assesses pain, disability, and work status. The primary outcome is duration of work disability over the year after claim submission, as assessed by administrative data. Secondary outcomes include work disability status at one year, as assessed by both self-report and work disability compensation status (administrative records). A sample size of 1,800 workers with back injuries and 1,200 with CTS will provide adequate statistical power (0.96 for low back and 0.85 for CTS) to predict disability with an alpha of .05 (two-sided) and a hazard ratio of 1.2. Proportional hazards regression models will be constructed to determine the best combination of predictors of work disability duration at one year. Regression models will also be developed for the secondary outcomes.
In this article we present results from a 2-year comprehensive exposure assessment study that examined the particulate matter (PM) exposures and health effects in 108 individuals with and without chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), and asthma. The average personal exposures to PM with aerodynamic diameters < 2.5 microm (PM2.5) were similar to the average outdoor PM2.5 concentrations but significantly higher than the average indoor concentrations. Personal PM2.5 exposures in our study groups were lower than those reported in other panel studies of susceptible populations. Indoor and outdoor PM2.5, PM10 (PM with aerodynamic diameters < 10 microm), and the ratio of PM2.5 to PM10 were significantly higher during the heating season. The increase in outdoor PM10 in winter was primarily due to an increase in the PM2.5 fraction. A similar seasonal variation was found for personal PM2.5. The high-risk subjects in our study engaged in an equal amount of dust-generating activities compared with the healthy elderly subjects. The children in the study experienced the highest indoor PM2.5 and PM10 concentrations. Personal PM2.5 exposures varied by study group, with elderly healthy and CHD subjects having the lowest exposures and asthmatic children having the highest exposures. Within study groups, the PM2.5 exposure varied depending on residence because of different particle infiltration efficiencies. Although we found a wide range of longitudinal correlations between central-site and personal PM2.5 measurements, the longitudinal r is closely related to the particle infiltration efficiency. PM2.5 exposures among the COPD and CHD subjects can be predicted with relatively good power with a microenvironmental model composed of three microenvironments. The prediction power is the lowest for the asthmatic children.
A >7-year, time-series, epidemiologic study is ongoing in Spokane, Washington, to examine the associations between ambient particulate constituents or sources and health outcomes such as emergency department (ED) visits for asthma or respiratory problems. One of the hypotheses being tested is that particulate toxic metals are associated with these health outcomes. Spokane is a desirable city in which to conduct this study because of its relatively high concentrations of particulate matter, low concentrations of potentially confounding air pollutants, variability of particulate sources, and presence of several potential particulate metals sources. Daily fine- and coarse-fraction particulate samples are analyzed for metals via energy-dispersive X-ray fluorescence (EDXRF) and instrumental neutron activation analysis. Particulate sources are determined using receptor modeling, including chemical mass balancing and positive matrix factorization coupled with partial source contribution function analysis. Principal component analysis has also been used to examine the influence of sources on the daily variability of the chemical composition of particulate samples. Based upon initial analyses using the EDXRF elemental analyses, statistically significant associations were observed between ED visits for asthma and increased combustion products, air stagnation, and fine particulate Zn. Although there is a significant soil particulate component, increased crustal particulate levels were not found to be associated with ED visits for asthma. Further research will clarify whether there is an association between specific health outcomes and either coarse or fine particulate metal species.