Abstract  RATIONALE: Several studies have linked long-term exposure to particulate air pollution with increased cardiopulmonary mortality; only two have also examined incident circulatory disease. OBJECTIVES: To examine associations of individualized long-term exposures to particulate and gaseous air pollution with incident myocardial infarction (MI) and stroke, as well as all-cause and cause-specific mortality. METHODS: We estimated long-term residential air pollution exposure for over 100,000 participants in the California Teachers Study, a prospective cohort of female public school professionals. We linked geocoded residential addresses with inverse distance-weighted monthly pollutant surfaces for two measures of particulate matter and for several gaseous pollutants. We examined associations between exposure to these pollutants and risks of incident MI and stroke, and of all-cause and cause-specific mortality, using Cox proportional hazards models. MEASUREMENTS AND MAIN RESULTS: We found elevated hazard ratios linking long-term exposure to fine particulate matter (PM2.5, scaled to an increment of 10 µg/m3) with mortality from ischemic heart disease (IHD) (1.20, 95% C.I. 1.02-1.41) and, particularly among post-menopausal women, incident stroke (1.19, 95% C.I. 1.02-1.38). Long-term exposure to particulate matter less than 10 µm aerodynamic diameter (PM10) was associated with elevated risks for IHD mortality (1.06, 95% C.I. 0.99-1.14) and incident stroke 1.06 (95% CI: 1.00-1.13), while nitrogen oxides were associated with elevated risks for cardiovascular as well as IHD mortality. CONCLUSIONS: Long-term exposures to PM2.5 and PM10 were associated with increased risks of incident stroke and death from IHD; exposures to nitrogen oxides were associated with all cardiovascular as well as IHD mortality.

Abstract  #Using the photographs and optical measurements taken during a summer field program (REVEAL) designed to examine the chemical and physical characteristics of visibility impairment in the Fraser Valley, southwestern British Columbia, a protocol for gauging public perception of visibility in this region was devised and tested in a pilot survey. This paper details the protocol (which is based on previous studies conducted in the United States of America) and analysis techniques for survey responses. A preliminary assessment of the results of the pilot study is also presented. A public perception survey conducted in Denver (Ely et al., 1991), resolved a visibility standard for Denver of bext (total light extinction) = 0.076 Î 10-3 m-1. Assuming a homogeneous atmosphere, this level of bext is approximately equal to a visual range of 50 km. Using a similar protocol, responses from this pilot study were used to extrapolate visibility standards for two suburban locations in the Fraser Valley of b(ext) (approx) 0.09 Î 10-3-0.105 Î 10-3 m-1 (bsp (particle light scattering) (approx) 0.051 Î 10-3-0.063 Î 10-3 m-1) and b(sp) (approx) 0.39 Î 10-3 m-1. These levels of light extinction (bsp is the largest component of b(ext)) relate to approximate visual range of between 40 and 60 km in a homogeneous atmosphere. Possible reasons for the apparent discrepancies between locations are discussed and the effect of survey group are addressed.

Abstract  Background and Methods Atrial fibrillation is associated with an increased risk of ischemic stroke. Data on individual patients were pooled from five recently completed randomized trials comparing warfarin (all studies) or aspirin (the Atrial Fibrillation, Aspirin, Anticoagulation Study and the Stroke Prevention in Atrial Fibrillation Study) with control in patients with atrial fibrillation. The purpose of the analysis was to (1) identify patient features predictive of a high or low risk of stroke, (2) assess the efficacy of antithrombotic therapy in major patient subgroups (eg, women), and (3) obtain the most precise estimate of the efficacy and risks of antithrombotic therapy in atrial fibrillation. For the warfarin-control comparison there were 1889 patient-years receiving warfarin and 1802 in the control group. For the aspirin-placebo comparison there were 1132 patient-years receiving aspirin and 1133 receiving placebo. The daily dose of aspirin was 75 mg in the Atrial Fibrillation, Aspirin, Anticoagulation Study and 325 mg in the Stroke Prevention in Atrial Fibrillation Study. To monitor warfarin dosage, three studies used prothrombin time ratios and two used international normalized ratios. The lowest target intensity was a prothrombin time ratio of 1.2 to 1.5 and the highest target intensity was an international normalized ratio of 2.8 to 4.2. The primary end points were ischemic stroke and major hemorrhage, as assessed by each study. Results At the time of randomization the mean age was 69 years and the mean blood pressure was 142/82 mm Hg. Forty-six percent of the patients had a history of hypertension, 6% had a previous transient ischemic attack or stroke, and 14% had diabetes. Risk factors that predicted stroke on multivariate analyses in control patients were increasing age, history of hypertension, previous transient ischemic attack or stroke, and diabetes. Patients younger than 65 years who had none of the other predictive factors (15% of all patients) had an annual rate of stroke of 1.0%, 95% confidence interval (CI) 0.3% to 3.0%. The annual rate of stroke was 4.5% for the control group and 1.4% for the warfarin group (risk reduction, 68%; 95% CI, 50% to 79%). The efficacy of warfarin was consistent across all studies and subgroups of patients. In women, warfarin decreased the risk of stroke by 84% (95% CI, 55% to 95%) compared with 60% (95% CI, 35% to 76%) in men. The efficacy of aspirin was not as consistent. The risk reduction with 75 mg of aspirin in the Atrial Fibrillation, Aspirin, Anticoagulation Study was 18% (95% CI, 60% to 58%), and with 325 mg of aspirin in the Stroke Prevention in Atrial Fibrillation Study the risk reduction was 44% (95% CI, 7% to 66%). When both studies were combined the risk reduction was 36% (95% CI, 4% to 57%). The annual rate of major hemorrhage (intracranial bleeding or a bleed requiring hospitalization or 2 units of blood) was 1.0% for the control group, 1.0% for the aspirin group, and 1.3% for the warfarin group. Conclusion In these five randomized trials warfarin consistently decreased the risk of stroke in patients with atrial fibrillation (a 68% reduction in risk) with virtually no increase in the frequency of major bleeding. Patients with atrial fibrillation younger than 65 years without a history of hypertension, previous stroke or transient ischemic attack, or diabetes were at very low risk of stroke even when not treated. The efficacy of aspirin was less consistent. Further studies are needed to clarify the role of aspirin in atrial fibrillation.

Abstract  Spatial estimations are increasingly used to estimate geocoded ambient particulate matter (PM) concentrations in epidemiologic studies because measures of daily PM concentrations are unavailable in most U.S. locations. This study was conducted to a) assess the feasibility of large-scale kriging estimations of daily residential-level ambient PM concentrations, b) perform and compare cross-validations of different kriging models, c) contrast three popular kriging approaches, and d) calculate SE of the kriging estimations. We used PM data for PM with aerodynamic diameter </= 10 microm (PM10) and aerodynamic diameter </= 2.5 microm (PM2.5) from the U.S. Environmental Protection Agency for the year 2000. Kriging estimations were performed at 94,135 geocoded addresses of Women's Health Initiative study participants using the ArcView geographic information system. We developed a semiautomated program to enable large-scale daily kriging estimation and assessed validity of semivariogram models using prediction error (PE) , standardized prediction error (SPE) , root mean square standardized (RMSS) , and SE of the estimated PM. National- and regional-scale kriging performed satisfactorily, with the former slightly better. The average PE, SPE, and RMSS of daily PM10 semivariograms using regular ordinary kriging with a spherical model were 0.0629, -0.0011, and 1.255 microg/m3, respectively ; the average SE of the estimated residential-level PM10 was 27.36 microg/m3. The values for PM2.5 were 0.049, 0.0085, 1.389, and 4.13 microg/m3, respectively. Lognormal ordinary kriging yielded a smaller average SE and effectively eliminated out-of-range predicted values compared to regular ordinary kriging. Semiautomated daily kriging estimations and semivariogram cross-validations are feasible on a national scale. Lognormal ordinary kriging with a spherical model is valid for estimating daily ambient PM at geocoded residential addresses.

Abstract  The U.S. Environmental Protection Agency (EPA) is conducting a review of the existing air quality criteria for particulate matter (PM) and of the primary (health-based) and secondary (welfare-based) national ambient air quality standards (NAAQS) for PM. This review will provide an integrative assessment of relevant scientific information on PM and will focus on the basic elements of the PM NAAQS: the indicator, averaging time, form, and level. These elements, which together serve to define each NAAQS, are considered collectively in evaluating the protection to public health and public welfare afforded by the standards. The purpose of this Integrated Review Plan (IRP) is to communicate the plan for reviewing the air quality criteria and the primary and secondary NAAQS for PM.

Abstract  Decreasing ambient fine particulate matter (PM2.5) concentrations over time together with increasing life expectancy raise concerns about temporal confounding of associations between PM2.5 and mortality. To address this issue, we examined PM2.5-associated mortality risk ratios (MRRs) estimated for approximately 20,000,000 US Medicare beneficiaries, who lived within six miles of an Environmental Protection Agency air quality monitoring site, between December 2000 and December 2012. We assessed temporal confounding by examining whether PM2.5-associated MRRs vary by study period length. We then evaluated three approaches to control for temporal confounding: (1) assessing exposures using the residual of PM2.5 regressed on time; (2) adding a penalized spline term for time to the health model; and (3) including a term that describes temporal variability in PM2.5 into the health model, with this term estimated using decomposition approaches. We found a 10 μg/m3 increase in PM2.5 exposure to be associated with a 1.20 times (95% confidence interval [CI] = 1.20, 1.21) higher risk of mortality across the 13-year study period, with the magnitude of the association decreasing with shorter study periods. MRRs remained statistically significant but were attenuated when models adjusted for long-term time trends in PM2.5. The residual-based, time-adjusted MRR equaled 1.12 (95% CI = 1.11, 1.12) per 10 μg/m3 for the 13-year study period and did not change when shorter study periods were examined. Spline- and decomposition-based approaches produced similar but less-stable MRRs. Our findings suggest that epidemiological studies of long-term PM2.5 can be confounded by long-term time trends, and this confounding can be controlled using the residuals of PM2.5 regressed on time.

Abstract  Assessing whether long-term exposure to air pollution increases the severity of COVID-19 health outcomes, including death, is an important public health objective. Limitations in COVID-19 data availability and quality remain obstacles to conducting conclusive studies on this topic. At present, publicly available COVID-19 outcome data for representative populations are available only as area-level counts. Therefore, studies of long-term exposure to air pollution and COVID-19 outcomes using these data must use an ecological regression analysis, which precludes controlling for individual-level COVID-19 risk factors. We describe these challenges in the context of one of the first preliminary investigations of this question in the United States, where we found that higher historical PM2.5 exposures are positively associated with higher county-level COVID-19 mortality rates after accounting for many area-level confounders. Motivated by this study, we lay the groundwork for future research on this important topic, describe the challenges, and outline promising directions and opportunities.

Abstract  Various approaches have been proposed to model PM2.5 in the recent decade, with satellite-derived aerosol optical depth, land-use variables, chemical transport model predictions, and several meteorological variables as major predictor variables. Our study used an ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM2.5 at a resolution of 1 km × 1 km across the contiguous United States. We used a generalized additive model that accounted for geographic difference to combine PM2.5 estimates from neural network, random forest, and gradient boosting. The three machine learning algorithms were based on multiple predictor variables, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis datasets, and others. The model training results from 2000 to 2015 indicated good model performance with a 10-fold cross-validated R2 of 0.86 for daily PM2.5 predictions. For annual PM2.5 estimates, the cross-validated R2 was 0.89. Our model demonstrated good performance up to 60 μg/m3. Using trained PM2.5 model and predictor variables, we predicted daily PM2.5 from 2000 to 2015 at every 1 km × 1 km grid cell in the contiguous United States. We also used localized land-use variables within 1 km × 1 km grids to downscale PM2.5 predictions to 100 m × 100 m grid cells. To characterize uncertainty, we used meteorological variables, land-use variables, and elevation to model the monthly standard deviation of the difference between daily monitored and predicted PM2.5 for every 1 km × 1 km grid cell. This PM2.5 prediction dataset, including the downscaled and uncertainty predictions, allows epidemiologists to accurately estimate the adverse health effect of PM2.5. Compared with model performance of individual base learners, an ensemble model would achieve a better overall estimation. It is worth exploring other ensemble model formats to synthesize estimations from different models or from different groups to improve overall performance.

Abstract  BACKGROUND: Epidemiological studies have consistently demonstrated that exposure to fine particulate matter (PM2.5) is associated with increased risks of mortality. To a lesser extent, a series of studies suggest that living in greener areas is associated with reduced risks of mortality. Only a handful of studies have examined the interplay between PM2.5, greenness, and mortality.

METHODS: We investigated the role of residential greenness in modifying associations between long-term exposures to PM2.5 and non-accidental and cardiovascular mortality in a national cohort of non-immigrant Canadian adults (i.e., the 2001 Canadian Census Health and Environment Cohort). Specifically, we examined associations between satellite-derived estimates of PM2.5 exposure and mortality across quintiles of greenness measured within 500 m of individual's place of residence during 11 years of follow-up. We adjusted our survival models for many personal and contextual measures of socioeconomic position, and residential mobility data allowed us to characterize annual changes in exposures.

RESULTS: Our cohort included approximately 2.4 million individuals at baseline, 194,270 of whom died from non-accidental causes during follow-up. Adjustment for greenness attenuated the association between PM2.5 and mortality (e.g., hazard ratios (HRs) and 95% confidence intervals (CIs) per interquartile range increase in PM2.5 in models for non-accidental mortality decreased from 1.065 (95% CI: 1.056-1.075) to 1.041 (95% CI: 1.031-1.050)). The strength of observed associations between PM2.5 and mortality decreased as greenness increased. This pattern persisted in models restricted to urban residents, in models that considered the combined oxidant capacity of ozone and nitrogen dioxide, and within neighbourhoods characterised by high or low deprivation. We found no increased risk of mortality associated with PM2.5 among those living in the greenest areas. For example, the HR for cardiovascular mortality among individuals in the least green areas was 1.17 (95% CI: 1.12-1.23) compared to 1.01 (95% CI: 0.97-1.06) among those in the greenest areas.

CONCLUSIONS: Studies that do not account for greenness may overstate the air pollution impacts on mortality. Residents in deprived neighbourhoods with high greenness benefitted by having more attenuated associations between PM2.5 and mortality than those living in deprived areas with less greenness. The findings from this study extend our understanding of how living in greener areas may lead to improved health outcomes.

Abstract  BACKGROUND: Many studies have identified an inequitable distribution of exposure to PM2.5 (particulate matter less than 2.5 microns) by race. We investigated the association of PM2.5 and cardiovascular mortality considering both the decedents' race and neighborhood racial composition as potential modifiers.

METHODS: We obtained geocoded cardiovascular mortality records of all black and white decedents from urban block-groups in Massachusetts between 2001 and 2011 (n = 130,863). We examined the association between PM2.5 and cardiovascular mortality, and assessed effect modification by three types of racial modifiers: decedents' race, census block-group percent black and white, and two novel measures of racial segregation. The Racial Residential Segregation (RRS) quantifies the concentration of non-Hispanic blacks and whites in each block-group. The Index of Racial Dissimilarity measures dissimilarity in non-Hispanic black and white racial distribution between the smaller census block-group and larger tract.

RESULTS: We found a 2.35%(95%CI: 0.92%;3.79%) increase in mortality for each 10μg/m3 increase in two-day average exposure to PM2.5. The effect was modified by the block-group racial composition, with higher risks in block-groups with the highest percentage of black residents (interaction p-value = 0.04), and in block-groups with the lowest RRS (i.e. higher black to white resident ratio, interaction p-value = 0.072). Racial dissimilarity did not modify the associations.

CONCLUSION: Current levels of PM2.5 are associated with increased cardiovascular deaths in Massachusetts, with different risks between areas with different racial composition and segregation. This suggests that pollution reductions in neighborhoods with the highest percentage of non-Hispanic blacks would be most beneficial in reducing cardiovascular mortality and disparities.

Abstract  The Integrated Science Assessment (ISA) for Oxides of Nitrogen, Oxides of Sulfur and Particulate Matter Ecological Criteria is a comprehensive evaluation and synthesis of the most policy relevant science aimed at characterizing the ecological effects caused by oxides of nitrogen, oxides of sulfur, and particulate matter. The ISA provides the scientific foundation necessary for the review of ecological effects associated with the secondary (welfare based) National Ambient Air Quality Standards (NAAQS) for these three criteria pollutants under the Clean Air Act. Welfare effects according to the Clean Air Act include, but are not limited to, effects on soils, water, crops, vegetation, animals, wildlife and climate. Oxides of nitrogen, oxides of sulfur, and particulate matter are three of six criteria pollutants for which EPA has established NAAQS. Periodically, EPA reviews the scientific basis for these standards by preparing an ISA (formerly called an Air Quality Criteria Document). The intent of the ISA, as described in the Clean Air Act (CAA), is to 'accurately reflect the latest scientific knowledge expected from the presence of [a] pollutant in ambient air.' It includes scientific research from atmospheric sciences, exposure and deposition, biogeochemistry, hydrology, soil science, marine science, plant physiology, animal physiology, and ecology conducted at multiple scales (e.g., population, community, ecosystem, landscape levels). Key information and judgments formerly found in the Air Quality Criteria Documents for oxides of sulfur, oxides of nitrogen and particulate matter for ecological effects are included; appendices provide additional details supporting the ISA. Together, the ISA and appendices serve to update and revise the 2008 Integrated Science Assessment for Oxides of Nitrogen and Oxides of Sulfur - Ecological Criteria and the ecological portion of the last particulate matter ISA which was published in 2009. Additionally, the Clean Air Scientific Advisory Committee (CASAC) is an independent science advisory committee whose review and advisory functions are mandated by Section 109(d)(2) of the Clean Air Act, and charged (among other things) with performing an independent scientific review of all the EPA’s air quality criteria.

Abstract  We used a geographically weighted regression (GWR) statistical model to represent bias of fine particulate matter concentrations (PM2.5) derived from a 1 km optimal estimate (OE) aerosol optical depth (AOD) satellite retrieval that used AOD-to-PM2.5 relationships from a chemical transport model (CTM) for 2004-2008 over North America. This hybrid approach combined the geophysical understanding and global applicability intrinsic to the CTM relationships with the knowledge provided by observational constraints. Adjusting the OE PM2.5 estimates according to the GWR-predicted bias yielded significant improvement compared with unadjusted long-term mean values (R(2) = 0.82 versus R(2) = 0.62), even when a large fraction (70%) of sites were withheld for cross-validation (R(2) = 0.78) and developed seasonal skill (R(2) = 0.62-0.89). The effect of individual GWR predictors on OE PM2.5 estimates additionally provided insight into the sources of uncertainty for global satellite-derived PM2.5 estimates. These predictor-driven effects imply that local variability in surface elevation and urban emissions are important sources of uncertainty in geophysical calculations of the AOD-to-PM2.5 relationship used in satellite-derived PM2.5 estimates over North America, and potentially worldwide.

Abstract  OBJECTIVE: Long-term exposure to traffic and particulate matter air pollution is associated with a higher risk of cardiovascular disease, potentially via atherosclerosis promotion. Prior research on associations of traffic and particulate matter with coronary artery calcium Agatston score (CAC), an atherosclerosis correlate, has yielded inconsistent findings. Given this background, we assessed whether residential proximity to major roadway or fine particulate matter were associated with CAC in a Northeastern US study.

APPROACH AND RESULTS: We measured CAC ≤2 times from 2002 to 2005 and 2008 to 2011 among Framingham Offspring or Third-Generation Cohort participants. We assessed associations of residential distance to major roadway and residential fine particulate matter (2003 average; spatiotemporal model) with detectable CAC, using generalized estimating equation regression. We used linear mixed effects models to assess associations with loge(CAC). We also assessed associations with CAC progression. Models were adjusted for demographic variables, socioeconomic position markers, and time. Among 3399 participants, 51% had CAC measured twice. CAC was detectable in 47% of observations. At first scan, mean age was 52.2 years (standard deviation 11.7); 51% male. There were no consistent associations with detectable CAC, continuous CAC, or CAC progression. We observed heterogeneous associations of distance to major roadway with odds of detectable CAC by hypertensive status; interpretation of these findings is questionable.

CONCLUSIONS: Our findings add to prior work and support evidence against strong associations of traffic or fine particulate matter with the presence, extent, or progression of CAC in a region with relatively low levels of and little variation in fine particulate matter.

Abstract  BACKGROUND: The effect of air pollution exposure on atherosclerosis severity or incident clinical events in patients with coronary artery disease is not known.

METHODS AND RESULTS: We conducted a prospective longitudinal cohort study of 6575 Ohio residents undergoing elective diagnostic coronary angiography. Multinomial regression and Cox proportional hazards models were used to assess the relationship between exposure to fine particulate matter <2.5 μm in diameter (PM2.5) and nitrogen dioxide on coronary artery disease severity at baseline and risk of myocardial infarction, stroke, or all-cause mortality over 3 years of follow-up. Among participants with coronary artery disease, exposure to PM2.5 levels was associated with increased likelihood of having coronary atherosclerosis that was mild (odds ratio 1.43, 95% CI 1.11-1.83, P=0.005) and severe (odds ratio 1.63, 95% CI 1.26-2.11, P<0.0001), with the effect on severe coronary artery disease being significantly increased compared with mild disease (Ptrend=0.03). Exposure to higher PM2.5 levels was also significantly associated with increased risk of incident myocardial infarction (hazard ratio 1.33, 95% CI 1.02-1.73, P=0.03) but not stroke or all-cause mortality. The association of PM2.5 with incident myocardial infarction was not affected after adjustment for Framingham Adult Treatment Panel III (ATP III) risk score or statin therapy. In comparison, there were no significant associations between nitrogen dioxide levels and all-cause mortality or risk of stroke after adjustment for Framingham ATP III risk score.

CONCLUSIONS: Exposure to PM2.5 increased the likelihood of having severe coronary artery disease and the risk of incident myocardial infarction among patients undergoing elective cardiac evaluation. These results suggest that ambient air pollution exposure may be a modifiable risk factor for risk of myocardial infarction in a highly susceptible patient population.

Abstract  This report describes the results of a study of long-term effects of PM components in the American Cancer Society's Cancer Prevention Study II cohort; a time-series study of short-term effects of PM components on cardiovascular and other diseases in people living in 150 U.S. cities; and two toxicologic studies in animals exposed by inhalation to concentrated ambient particles, and in animals and human cells exposed to particles collected on filters from five different airsheds across the United States. This report, along with Research Report 178 (Vedal et al.), is one of HEI's National Particle Component Toxicity (NPACT) studies, which describe the most systematic multidisciplinary studies to date to investigate the health effects of PM components in humans and animal models at locations across the United States where the effects of PM sources and components may differ. The report includes a Commentary and a Synthesis by the NPACT Review Panel.

Abstract  Visible Multifilter Rotating Shadowband Radiometer (vis-MFRSR) data were collected at Storm Peak Laboratory (SPL), a mountain-top facility in northwest Colorado, from 1999 to 2011 and in 2013. From 2011 to 2014, in situ measurements of aerosol light scattering were also obtained. Using these data sets together, the seasonal impact of dust and biomass burning is considered for the western USA. Analysis indicates that the median contributions to spring and summer aerosol optical depth (AOD) from dust and biomass-burning aerosols across the data set are comparable. The mean AOD is slightly greater in the summer, with significantly more frequent and short-duration high AOD measurements due to biomass-burning episodes than in the spring. The angstrom ngstrom exponent showed a significant increase in the summer for both the in situ and vis-MFRSR data, suggesting an increase in combustion aerosols. Spring dust events are less distinguishable in the in situ data than the column measurement, suggesting that a significant amount of dust may be found above the elevation of SPL, 3220 ma.s.l. Twenty-two known case studies of intercontinental dust, regional dust, and biomass-burning events were investigated. These events were found to follow a similar pattern, in both aerosol loading and angstrom ngstrom exponent, as the seasonal mean signal in both the vis-MFRSR and ground-based nephelometer. This data set highlights the wide-scale implications of a warmer, drier climate on visibility in the western USA.

Abstract  Summary Background Long-term exposure to fine particulate matter less than 2·5 μm in diameter (PM2·5) and traffi c-related air pollutant concentrations are associated with cardiovascular risk. The disease process underlying these associations remains uncertain. We aim to assess association between long-term exposure to ambient air pollution and progression of coronary artery calcium and common carotid artery intima-media thickness. Methods In this prospective 10-year cohort study, we repeatedly measured coronary artery calcium by CT in 6795 participants aged 45–84 years enrolled in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) in six metropolitan areas in the USA. Repeated scans were done for nearly all participants between 2002 and 2005, for a subset of participants between 2005 and 2007, and for half of all participants between 2010 and 2012. Common carotid artery intimamedia thickness was measured by ultrasound in all participants at baseline and in 2010–12 for 3459 participants. Residence-specifi c spatio-temporal pollution concentration models, incorporating Community specific measurements, agency monitoring data, and geographical predictors, estimated concentrations of PM2·5 and nitrogen oxides (NOX) between 1999 and 2012. The primary aim was to examine the association between both progression of coronary artery calcium and mean carotid artery intima-media thickness and long-term exposure to ambient air pollutant concentrations (PM2·5, NOX, and black carbon) between examinations and within the six metropolitan areas, adjusting for baseline age, sex, ethnicity, socioeconomic characteristics, cardiovascular risk factors, site, and CT scanner technology. Findings In this population, coronary calcium increased on average by 24 Agatston units per year (SD 58), and intimamedia thickness by 12 μm per year (10), before adjusting for risk factors or air pollutant exposures. Participant specific pollutant concentrations averaged over the years 2000–10 ranged from 9·2–22·6 µg PM2·5/m³ and 7·2–139·2 parts per billion (ppb) NOX. For each 5 µg PM2·5/m³ increase, coronary calcium progressed by 4·1 Agatston units per year(95% CI 1·4–6·8) and for each 40 ppb NOX coronary calcium progressed by 4·8 Agatston units per year (0·9–8·7). Pollutant exposures were not associated with intima-media thickness change. The estimate for the effect of a 5 µg/m³ higher long-term exposure to PM2·5 in intima-media thickness was –0·9 µm per year (95% CI –3·0 to 1·3). For 40 ppb higher NOX, the estimate was 0·2 µm per year (–1·9 to 2·4). Interpretation Increased concentrations of PM2·5 and traffic-related air pollution within metropolitan areas, in ranges commonly encountered worldwide, are associated with progression in coronary calcifi cation, consistent with acceleration of atherosclerosis. This study supports the case for global eff orts of pollution reduction in prevention of cardiovascular diseases.

Abstract  The impact of chronic exposure to fine particulate matter (PM2.5) on respiratory disease and lung cancer mortality is poorly understood. In a cohort of 18.9 million Medicare beneficiaries (4.2 million deaths) living across the conterminous United States between 2000 to 2008, we examined the association of chronic PM2.5 exposure and cause-specific mortality, and evaluated confounding through adjustment of neighborhood behavioral covariates and decomposition of PM2.5 into two spatiotemporal scales. We found significantly positive associations of 12-month moving average PM2.5 exposures (per 10 ug/m3 increase) with respiratory, chronic obstructive pulmonary disease and pneumonia mortality, with risk ratios ranging from 1.10 to 1.24. We also found significant PM2.5-associated elevated risks for cardiovascular-related and lung cancer mortality. Risk ratios generally increased with longer moving averages; e.g., elevation in 60-month moving averaged PM2.5 exposures was linked to 1.33 times the lung cancer mortality risk (95% confidence interval: 1.24, 1.40), as compared to 1.13 (95% confidence interval: 1.11, 1.15) for 12-month moving averaged exposures. Observed associations were robust in multivariable models, although evidence of unmeasured confounding remained. In our large cohort of American elderly, we provide important new evidence that long-term PM2.5 exposure is significantly related to increased respiratory-, lung cancer and cardiovascular-related mortality.

Abstract  Fine particulate matter is associated with adverse health outcomes. Exposure to fine particulate matter may disproportionately affect urban communities with larger numbers of vulnerable residents. We used multilevel logistic regression models to estimate the joint effects of fine particulate matter (PM2.5) and population vulnerabilities on cardiopulmonary mortality (CPM). We estimated the health benefits of reductions in PM2.5 across census tracts in the Detroit metropolitan area with varying levels of population vulnerability, using cluster-specific odds ratios scaled to reflect PM2.5-attributable cardiopulmonary risk. PM2.5 and population vulnerability were independently associated with odds of CPM. Odds of CPM and the number of deaths attributable to PM2.5 were greatest in census tracts with both high PM2.5 exposures and population vulnerability. Reducing PM2.5 in census tracts with high PM2.5 would lead to an estimated 18% annual reduction in PM2.5-attributable CPM. Between 78⁻79% of those reductions in CPM would occur within census tracts with high population vulnerabilities. These health benefits of reductions in PM2.5 occurred at levels below current U.S. reference concentrations. Focusing efforts to reduce PM2.5 in the Detroit metropolitan area in census tracts with currently high levels would also lead to greater benefits for residents of census tracts with high population vulnerabilities.

Abstract  Mounting evidence over the past several decades has demonstrated inequitable distribution of pollutants of ambient origin between sociodemographic groups in the United States. Most environmental inequality studies to date are cross-sectional and used proximity-based methods rather than modeled air pollution concentrations, limiting the ability to examine trends over time or the factors that drive exposure inequalities. In this paper, we use 1km2 modeled PM2.5 and NO2 concentrations in Massachusetts over an 8-year period and Census demographic data to quantify inequality between sociodemographic groups and to develop a more nuanced understanding of the drivers and trends in longitudinal air pollution inequality. Annual-average population-weighted PM2.5 and NO2 concentrations were highest for urban non-Hispanic black populations (11.8µg/m3 in 2003 and 8.4µg/m3 in 2010, vs. 11.3µg/m3 and 8.1µg/m3 for urban non-Hispanic whites) and urban Hispanic populations (15.9 ppb in 2005 and 13.0 ppb in 2010, vs. 13.0 ppb and 10.2 ppb for urban non-Hispanic whites), respectively. While population groups experienced similar absolute decreases in exposure over time, disparities in population-weighted concentrations increased over time when quantified by the Atkinson Index, a relative inequality measure. Exposure inequalities were approximately one order of magnitude greater for NO2 compared to PM2.5, were more pronounced in urban compared to rural geographies, and between racial/ethnic groups compared to income and educational attainment groups. Our results also revealed similar longitudinal PM2.5 and NO2 inequality trends using Census 2000 and Census 2010 data, indicating that spatio-temporal shifts in air pollution may best explain observed trends in inequality. These findings enhance our understanding of factors that contribute to persistent inequalities and underscore the importance of targeted exposure reduction strategies aimed at vulnerable populations and neighborhoods.

Abstract  OBJECTIVES: Develop statistical methods for survival models to indirectly adjust hazard ratios of environmental exposures for missing risk factors.

METHODS: A partitioned regression approach for linear models is applied to time to event survival analyses of cohort study data. Information on the correlation between observed and missing risk factors is obtained from ancillary data sources such as national health surveys. The relationship between the missing risk factors and survival is obtained from previously published studies. We first evaluated the methodology using simulations, by considering the Weibull survival distribution for a proportional hazards regression model with varied baseline functions, correlations between an adjusted variable and an adjustment variable as well as selected censoring rates. Then we illustrate the method in a large, representative Canadian cohort of the association between concentrations of ambient fine particulate matter and mortality from ischemic heart disease.

RESULTS: Indirect adjustment for cigarette smoking habits and obesity increased the fine particulate matter-ischemic heart disease association by 3%-123%, depending on the number of variables considered in the adjustment model due to the negative correlation between these two risk factors and ambient air pollution concentrations in Canada. The simulations suggested that the method yielded small relative bias (<40%) for most cohort designs encountered in environmental epidemiology.

CONCLUSIONS: This method can accommodate adjustment for multiple missing risk factors simultaneously while accounting for the associations between observed and missing risk factors and between missing risk factors and health endpoints.

Abstract  Epidemiologic studies on acute effects of air pollution have generally been limited to larger cities, leaving questions about rural populations behind. Recently, we had developed a spatiotemporal model to predict daily PM2.5 level at a 1 km(2) using satellite aerosol optical depth (AOD) data. Based on the results from the model, we applied a case-crossover study to evaluate the acute effect of PM2.5 on mortality in North Carolina, South Carolina, and Georgia between 2007 and 2011. Mortality data were acquired from the Departments of Public Health in the States and modeled PM2.5 exposures were assigned to the zip code of residence of each decedent. We performed various stratified analyses by age, sex, race, education, cause of death, residence, and environmental protection agency (EPA) standards. We also compared results of analyses using our modeled PM2.5 levels and those imputed daily from the nearest monitoring station. 848,270 non-accidental death records were analyzed and we found each 10 μg/m(3) increase in PM2.5 (mean lag 0 and lag 1) was associated with a 1.56% (1.19 and 1.94) increase in daily deaths. Cardiovascular disease (2.32%, 1.57-3.07) showed the highest effect estimate. Blacks (2.19%, 1.43-2.96) and persons with education ≤8 year (3.13%, 2.08-4.19) were the most vulnerable populations. The effect of PM2.5 on mortality still exists in zip code areas that meet the PM2.5 EPA annual standard (2.06%, 1.97-2.15). The effect of PM2.5 below both EPA daily and annual standards was 2.08% (95% confidence interval=1.99-2.17). Our results showed more power and suggested that the PM2.5 effects on rural populations have been underestimated due to selection bias and information bias. We have demonstrated that our AOD-based exposure models can be successfully applied to epidemiologic studies. This will add new study populations in rural areas, and will confer more generalizability to conclusions from such studies.Journal of Exposure Science and Environmental Epidemiology advance online publication, 26 August 2015; doi:10.1038/jes.2015.47.