Abstract  Background: Dozens of cohort studies have associated particulate matter smaller than 2.5 µm in diameter (PM2.5) exposure with early deaths, and the Global Burden of Disease identified PM2.5 as the fifth-ranking mortality risk factor in 2015. However, few studies have used causal modeling techniques. We assessed the effect of annual PM2.5 exposure on all-cause mortality rates among the Medicare population in the Northeastern and mid-Atlantic states, using the difference-in-differences approach for causal modeling.

Methods: We obtained records of Medicare beneficiaries 65 years of age or more who reside in the Northeastern or mid-Atlantic states from 2000 to 2013 and followed each participant from the year of enrollment to the last year of follow-up. We estimated the causal effect of annual PM2.5 exposure on mortality rates using the difference-in-differences approach in the Poisson survival analysis. We controlled for individual confounders, for spatial differences using dummy variables for each ZIP code and for time trends using a penalized spline of year.

Results: We included 112,376,805 person-years from 15,401,064 people, of whom 37.4% died during the study period. The interquartile range (IQR) of the annual PM2.5 concentration was 3 µg/m3, and the mean annual PM2.5 concentration ranged between 6.5 and 14.5 µg/m3 during the study period. An IQR incremental increase in PM2.5 was associated with a 4.04% increase (95% CI = 3.49%, 4.59%) in mortality rates.

Conclusions: Assuming no omitted predictors changing differently across ZIP codes over time in correlation with PM2.5, we found a causal effect of PM2.5 on mortality incidence rate.

Abstract  We propose a new approach for estimating causal effects when the exposure is measured with error and confounding adjustment is performed via a generalized propensity score (GPS). Using validation data, we propose a regression calibration (RC)-based adjustment for a continuous error-prone exposure combined with GPS to adjust for confounding (RC-GPS). The outcome analysis is conducted after transforming the corrected continuous exposure into a categorical exposure. We consider confounding adjustment in the context of GPS subclassification, inverse probability treatment weighting (IPTW) and matching. In simulations with varying degrees of exposure error and confounding bias, RC-GPS eliminates bias from exposure error and confounding compared to standard approaches that rely on the error-prone exposure. We applied RC-GPS to a rich data platform to estimate the causal effect of long-term exposure to fine particles (PM2.5) on mortality in New England for the period from 2000 to 2012. The main study consists of 2202 zip codes covered by 217,660 1 km × 1 km grid cells with yearly mortality rates, yearly PM2.5 averages estimated from a spatio-temporal model (error-prone exposure) and several potential confounders. The internal validation study includes a subset of 83 1 km × 1 km grid cells within 75 zip codes from the main study with error-free yearly PM2.5 exposures obtained from monitor stations. Under assumptions of noninterference and weak unconfoundedness, using matching we found that exposure to moderate levels of PM2.5 (8 < PM2.5 ≤ 10 μg/m3) causes a 2.8% (95% CI: 0.6%, 3.6%) increase in all-cause mortality compared to low exposure (PM2.5 ≤ 8 μg/m3).

Abstract  Air quality monitoring has traditionally been conducted using sparsely distributed, expensive reference monitors. To understand variations in PM2.5 on a finely resolved spatiotemporal scale a dense network of over 40 low-cost monitors was deployed throughout and around Pittsburgh, Pennsylvania, USA. Monitor locations covered a wide range of site types with varying traffic and restaurant density, varying influences from local sources, and varying socioeconomic (environmental justice, EJ) characteristics. Variability between and within site groupings was observed. Concentrations were higher near the source-influenced sites than the Urban or Suburban Residential sites. Gaseous pollutants (NO2 and SO2) were used to differentiate between traffic (higher NO2 concentrations) and industrial (higher SO2 concentrations) sources of PM2.5. Statistical analysis proved these differences to be significant (coefficient of divergence > 0.2). The highest mean PM2.5 concentrations were measured downwind (east) of the two industrial facilities while background level PM2.5 concentrations were measured at similar distances upwind (west) of the point sources. Socioeconomic factors, including the fraction of non-white population and fraction of population living under the poverty line, were not correlated with increases in PM2.5 or NO2 concentration. The analysis conducted here highlights differences in PM2.5 concentration within site groupings that have similar land use thus demonstrating the utility of a dense sensor network. Our network captures temporospatial pollutant patterns that sparse regulatory networks cannot.

Abstract  Multiwavelength light attenuation measurements have been acquired as part of thermal/optical carbon analysis in the U.S. Chemical Speciation Network (CSN) and the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network beginning in 2016. These are used to estimate PM2.5 brown carbon (BrC) contributions to light absorption at various wavelengths, a useful method for separating biomass burning contributions from other sources. Attenuation of light transmitted through the filter deviates from Beers Law as the mass of light absorbing materials increase. This study estimates the effects of these deviations with empirical adjustment factors applied to samples for CSN from 2016 to 2017 and for IMPROVE from 2016 to 2019. Accounting for the filter loading effect results in an annual average increase of ∼6–7% BrC contribution to light attenuation: from 3.6% to 10.7% for the urban, more heavily loaded CSN samples; and from 23.7% to 29.5% for the non-urban IMPROVE samples. An alternative method is examined for BrC and black carbon (BC) adjustments by calculating the Absorption Ångström Exponent (AAE) for BC (i.e., AAEBC) based on the ratios of 635 nm/780 nm light attenuation rather than assuming AAEBC of unity. These paired-wavelength calculations result in a median AAEBC of 0.76 for CSN and 0.8 for IMPROVE, with the majority of samples (i.e., 91% of CSN and 70% of IMPROVE) showing AAEBC < 1. By assuming negligible contributions from BrC to AAE at longer wavelengths, the amount of light attenuation at shorter wavelengths (e.g., 405 nm) where BrC is dominant can be calculated. The paired-wavelength method applied to the filter loading adjusted data has a greater effect on urban (fresh) than on non-urban (aged) aerosols, resulting in a factor of two increase in annual averaged BrC light attenuation (from 10.7% to 21.6%) for CSN and by a factor of 1.11 (from 29.5% to 32.7%) for IMPROVE samples. This result demonstrates the importance of particle loading and AAE correction on quantifying BrC light attenuation from multi-wavelength thermal/optical analysis.

Abstract  Background: The novel human coronavirus disease 2019 (COVID-19) pandemic has claimed more than 600,000 lives worldwide, causing tremendous public health, social, and economic damages. Although the risk factors of COVID-19 are still under investigation, environmental factors, such as urban air pollution, may play an important role in increasing population susceptibility to COVID-19 pathogenesis. Methods: We conducted a cross-sectional nationwide study using zero-inflated negative binomial models to estimate the association between long-term (2010-2016) county-level exposures to NO2, PM2.5, and O3 and county-level COVID-19 case-fatality and mortality rates in the United States. We used both single- and multi-pollutant models and controlled for spatial trends and a comprehensive set of potential confounders, including state-level test positive rate, county-level health care capacity, phase of epidemic, population mobility, population density, sociodemographics, socioeconomic status, race and ethnicity, behavioral risk factors, and meteorology. Results: From January 22, 2020, to July 17, 2020, 3,659,828 COVID-19 cases and 138,552 deaths were reported in 3,076 US counties, with an overall observed case-fatality rate of 3.8%. County-level average NO2 concentrations were positively associated with both COVID-19 case-fatality rate and mortality rate in single-, bi-, and tri-pollutant models. When adjusted for co-pollutants, per interquartile-range (IQR) increase in NO2 (4.6 ppb), COVID-19 case-fatality rate and mortality rate were associated with an increase of 11.3% (95% CI 4.9%-18.2%) and 16.2% (95% CI 8.7%-24.0%), respectively. We did not observe significant associations between COVID-19 case-fatality rate and long-term exposure to PM2.5 or O3, although per IQR increase in PM2.5 (2.6 μg/m3) was marginally associated, with a 14.9% (95% CI 0.0%-31.9%) increase in COVID-19 mortality rate when adjusted for co-pollutants. Discussion: Long-term exposure to NO2, which largely arises from urban combustion sources such as traffic, may enhance susceptibility to severe COVID-19 outcomes, independent of long-term PM2.5 and O3 exposure. The results support targeted public health actions to protect residents from COVID-19 in heavily polluted regions with historically high NO2 levels. Continuation of current efforts to lower traffic emissions and ambient air pollution may be an important component of reducing population-level risk of COVID-19 case fatality and mortality.

Abstract  BACKGROUND: Diabetes is infrequently coded as the primary cause of death but may contribute to cardiovascular disease (CVD) mortality in response to fine particulate matter (PM2.5) exposure. We analyzed all contributing causes of death to examine susceptibility of diabetics to CVD mortality from long-term exposure.

METHODS: We linked a subset of the 2001 Canadian Census Health and Environment Cohort (CanCHEC) with 10 years of follow-up to all causes of death listed on death certificates. We used survival models to examine the association between CVD deaths (n = 123,500) and exposure to PM2.5 among deaths that co-occurred with diabetes (n = 20,600) on the death certificate. More detailed information on behavioral covariates and diabetes status at baseline available in the Canadian Community Health Survey (CCHS)-mortality cohort (n = 12,400 CVD deaths, with 2,800 diabetes deaths) complemented the CanCHEC analysis.

RESULTS: Among CanCHEC subjects, comention of diabetes on the death certificate increased the magnitude of association between CVD mortality and PM2.5 (HR = 1.51 [1.39-1.65] per 10 μg/m) versus all CVD deaths (HR = 1.25 [1.21-1.29]) or CVD deaths without diabetes (HR = 1.20 [1.16-1.25]). Among CCHS subjects, diabetics who used insulin or medication (included as proxies for severity) had higher HR estimates for CVD deaths from PM2.5 (HR = 1.51 [1.08-2.12]) relative to the CVD death estimate for all respondents (HR = 1.31 [1.16-1.47]).

CONCLUSIONS: Mention of diabetes on the death certificate resulted in higher magnitude associations between PM2.5 and CVD mortality, specifically among those who manage their diabetes with insulin or medication. Analyses restricted to the primary cause of death likely underestimate the role of diabetes in air pollution-related mortality. See video abstract at, http://links.lww.com/EDE/B408.

Abstract  BACKGROUND: Hospitalization and mortality (H-M) have been linked to air pollution separately. However, previous studies have not adequately compared whether air pollution is a stronger risk factor for hospitalization or mortality. This study aimed to investigate differences in H-M risk from short-term ozone and PM2.5 exposures, and determine whether differences are modified by season, age, and sex.

METHODS: Daily ozone, PM2.5, temperature, and all-cause H-M counts (ICD-10, A00-R99) were collected for 22-24 Canadian cities for up to 29 years. Generalized additive Poisson models were employed to estimate associations between each pollutant and health outcome, which were compared across season (warm, cold, or year-round), age (all ages or seniors > 65), and sex.

RESULTS: Overall, ozone and PM2.5 showed higher season-specific risk of mortality than hospitalization: warm-season ozone: 0.54% (95% credible interval, 0.20, 0.85) vs. 0.14% (0.02, 0.27) per 10 ppb; and year-round PM2.5: 0.90% (0.33, 1.41) vs. 0.29% (0.03, 0.56) per 10 μg/m3. While age showed little H-M difference, sex appeared to be a modifier of H-M risk. While females had higher mortality risk, males had higher hospitalization risk: for females, ozone 0.87% (0.36, 1.35) vs. -0.03% (-0.18, 0.11) and PM2.5 1.19% (0.40, 1.90) vs. 0.19% (-0.10, 0.47); and for males ozone 0.20% (-0.28, 0.65) vs. 0.35% (0.18, 0.51).

CONCLUSION: This study found H-M differences attributable to ozone and PM2.5, suggesting that both are stronger risk factors for mortality than hospitalization. In addition, there were clear H-M differences by sex: specifically, females showed higher mortality risk and males showed higher hospitalization risk.

Abstract  An accurate fine-resolution surface of the chemical composition of fine particulate matter (PM2.5) would offer valuable information for epidemiological studies and health impact assessments. We develop geoscience-derived estimates of PM2.5 composition from a chemical transport model (GEOS-Chem) and satellite observations of aerosol optical depth, and statistically fuse these estimates with ground-based observations using a geographically weighted regression over North America to produce a spatially complete representation of sulfate, nitrate, ammonium, black carbon, organic matter, mineral dust, and sea-salt over 2000-2016. Significant long-term agreement is found with cross-validation sites over North America (R2 = 0.57-0.96), with the strongest agreement for sulfate (R2 = 0.96), nitrate (R2 = 0.90), and ammonium (R2 = 0.86). We find that North American decreases in population-weighted fine particulate matter (PM2.5) concentrations since 2000 have been most heavily influenced by regional changes in sulfate and organic matter. Regionally, the relative importance of several chemical components are found to change with PM2.5 concentration, such as higher PM2.5 concentrations having a larger proportion of nitrate and a smaller proportion of sulfate. This data set offers information for research into the health effects of PM2.5 chemical components.

Abstract  BACKGROUND: Stroke is a leading cause of morbidity and mortality in the United States. Associations between short-term exposures to particulate matter (PM) air pollution and stroke are inconsistent. Many prior studies have used administrative and hospitalization databases where misclassification of the type and timing of the stroke event may be problematic.

METHODS: In this case-crossover study, we used a nationwide kriging model to examine short-term ambient exposure to PM10 and PM2.5 and risk of ischemic and hemorrhagic stroke among men enrolled in the Health Professionals Follow-up Study. Conditional logistic regression models were used to obtain estimates of odds ratios (OR) and 95% confidence intervals (CI) associated with an interquartile range (IQR) increase in PM2.5 or PM10. Lag periods up to 3 days prior to the stroke event were considered in addition to a 4-day average. Stratified models were used to examine effect modification by patient characteristics.

RESULTS: Of the 727 strokes that occurred between 1999 and 2010, 539 were ischemic and 122 were hemorrhagic. We observed positive statistically significant associations between PM10 and ischemic stroke (ORlag0-3 = 1.26; 95% CI: 1.03-1.55 per IQR increase [14.46 μg/m3]), and associations were elevated for nonsmokers, aspirin nonusers, and those without a history of high cholesterol. However, we observed no evidence of a positive association between short-term exposure to PM and hemorrhagic stroke or between PM2.5 and ischemic stroke in this cohort.

CONCLUSIONS: Our study provides evidence that ambient PM10 may be associated with higher risk of ischemic stroke and highlights that ischemic and hemorrhagic strokes are heterogeneous outcomes that should be treated as such in analyses related to air pollution.

Abstract  Background: Differences in traffic-related air pollution (TRAP) composition may cause heterogeneity in associations between air pollution exposure and cardiovascular health outcomes. Clustering multi-pollutant measurements allows investigation of effect modification by TRAP profiles.

Methods: We measured TRAP components with fixed-site and on-road instruments for two two-week periods in Baltimore, Maryland. We created representative TRAP profiles for cold and warm seasons using predictive k-means clustering. We predicted cluster membership for 1005 participants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution with follow-up between 2000 and 2012. We estimated cluster-specific relationships between coronary artery calcification (CAC) progression and long-term exposure to fine particulate matter (PM2.5) and oxides of nitrogen (NOX).

Results: We identified two clusters in the cold season, notable for higher ratios of gases and ultrafine particles, respectively. A 5 μg/m3 difference in PM2.5 was associated with 17.0 (95% Confidence Interval [CI]: 7.2, 26.7) and 42.6 (95% CI: 25.7, 59.4) Agatston units/year CAC progression among participants in clusters 1 and 2, respectively (effect modification p=0.006). A 40ppb difference in NOX was associated with 22.2 (95% CI: 7.7, 36.7) and 41.9 (95% CI: 23.7, 60.2) Agatston units/year CAC progression in clusters 1 and 2, respectively (p=0.08). Similar trends occurred using clusters identified from warm season measurements. Clusters correlated highly with baseline pollution level.

Conclusions: Clustering TRAP measurements identified spatial differences in composition. We found evidence of greater CAC progression rates per unit PM2.5 exposures among people living in areas characterized by high ratios of ultrafine particle counts relative to NOX concentrations.

Abstract  Often, in environmental data collection, data arise from two sources: numerical models and monitoring networks. The first source provides predictions at the level of grid cells, while the second source gives measurements at points. The first is characterized by full spatial coverage of the region of interest, high temporal resolution, no missing data but consequential calibration concerns. The second tends to be sparsely collected in space with coarser temporal resolution, often with missing data but, where recorded, provides, essentially, the true value. Accommodating the spatial misalignment between the two types of data is of fundamental importance for both improved predictions of exposure as well as for evaluation and calibration of the numerical model. In this article we propose a simple, fully model-based strategy to downscale the output from numerical models to point level. The static spatial model, specified within a Bayesian framework, regresses the observed data on the numerical model output using spatially-varying coefficients which are specified through a correlated spatial Gaussian process.As an example, we apply our method to ozone concentration data for the eastern U.S. and compare it to Bayesian melding (Fuentes and Raftery 2005) and ordinary kriging (Cressie 1993; Chilès and Delfiner 1999). Our results show that our method outperforms Bayesian melding in terms of computing speed and it is superior to both Bayesian melding and ordinary kriging in terms of predictive performance; predictions obtained with our method are better calibrated and predictive intervals have empirical coverage closer to the nominal values. Moreover, our model can be easily extended to accommodate for the temporal dimension. In this regard, we consider several spatio-temporal versions of the static model. We compare them using out-of-sample predictions of ozone concentration for the eastern U.S. for the period May 1-October 15, 2001. For the best choice, we present a summary of the analysis. Supplemental material, including color versions of Figures 4, 5, 6, 7, and 8, and MCMC diagnostic plots, are available online.

Abstract  This Policy Assessment (PA) has been prepared by staff in the Environmental Protection Agency’s (EPA) Office of Air Quality Planning and Standards (OAQPS) in conjunction with the Agency’s ongoing review of the national ambient air quality standards (NAAQS) for particulate matter (PM), which include primary (health-based) and secondary (welfare-based) standards. It presents staff conclusions regarding the adequacy of the current suite of PM standards as well as potential alternative standards for consideration in this review. Staff conclusions are based on the scientific and technical information, as well as uncertainties and limitations related to this information, assessed in other EPA documents, including the Integrated Science Assessment for Particulate Matter (Final Report) (ISA, US EPA, 2009a), the Quantitative Health Risk Assessment for Particulate Matter (Final Report) (RA, US EPA, 2010a) and the Particulate Matter Urban-Focused Visibility Assessment (Final Report) (UFVA, US EPA, 2010b). This PA is intended to “bridge the gap” between the relevant scientific evidence and technical information and the judgments required of the EPA Administrator in determining whether, and if so how, to revise the PM NAAQS. The current and potential alternative PM standards are considered in terms of the basic elements of the NAAQS: indicator, averaging time, form, and level.

Abstract  BACKGROUND: Few cohort studies have evaluated the risk of mortality associated with long-term exposure to fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM(2.5))]. This is the first national-level cohort study to investigate these risks in Canada.

OBJECTIVE: We investigated the association between long-term exposure to ambient PM(2.5) and cardiovascular mortality in nonimmigrant Canadian adults.

METHODS: We assigned estimates of exposure to ambient PM(2.5) derived from satellite observations to a cohort of 2.1 million Canadian adults who in 1991 were among the 20% of the population mandated to provide detailed census data. We identified deaths occurring between 1991 and 2001 through record linkage. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) adjusted for available individual-level and contextual covariates using both standard Cox proportional survival models and nested, spatial random-effects survival models.

RESULTS: Using standard Cox models, we calculated HRs of 1.15 (95% CI: 1.13, 1.16) from nonaccidental causes and 1.31 (95% CI: 1.27, 1.35) from ischemic heart disease for each 10-μg/m(3) increase in concentrations of PM(2.5). Using spatial random-effects models controlling for the same variables, we calculated HRs of 1.10 (95% CI: 1.05, 1.15) and 1.30 (95% CI: 1.18, 1.43), respectively. We found similar associations between nonaccidental mortality and PM2.5 based on satellite-derived estimates and ground-based measurements in a subanalysis of subjects in 11 cities.

CONCLUSIONS: In this large national cohort of nonimmigrant Canadians, mortality was associated with long-term exposure to PM(2.5). Associations were observed with exposures to PM(2.5) at concentrations that were predominantly lower (mean, 8.7 μg/m(3); interquartile range, 6.2 μg/m(3)) than those reported previously.

Abstract  EPA announced the availability of the final report, Integrated Science Assessment of Ozone and Related Photochemical Oxidants. This document represents a concise synthesis and evaluation of the most policy-relevant science and will ultimately provide the scientific bases for EPA’s decision regarding the adequacy of the current national ambient air quality standards for ozone to protect human health, public welfare, and the environment. Ozone (O3) is one of six principal (or criteria) pollutants for which EPA has established national ambient air quality standards (NAAQS). The Clean Air Act requires EPA to periodically review the scientific basis for these standards by preparing an Integrated Science Assessment (ISA). The ISA, in conjunction with additional technical and policy assessments, provide the scientific basis for EPA decisions on the adequacy of the current NAAQS and the appropriateness of possible alternative standards. These reviews play a significant role in EPA’s commitment to ensuring a clean and healthy environment for the public.

Abstract  EPA has released the final Integrated Science Assessment (ISA) for Particulate Matter (PM). This is EPA’s latest evaluation of the scientific literature on the potential human health and welfare effects associated with ambient exposures to particulate matter (PM). The development of this document is part of the Agency's periodic review of the national ambient air quality standards (NAAQS) for PM. The recently completed PM ISA and supplementary annexes, in conjunction with additional technical and policy assessments developed by EPA’s Office of Air and Radiation, will provide the scientific basis to inform EPA decisions related to the review of the current PM NAAQS. PM is one of six principal (or 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 ISA and supplementary annexes, in conjunction with additional technical and policy assessments, provide the scientific basis for EPA decisions on the adequacy of the current NAAQS and the appropriateness of possible alternative standards. The Clean Air Scientific Advisory Committee (CASAC), an independent science advisory committee whose existence and whose review and advisory functions are mandated by Section 109 (d) (2) of the Clean Air Act, is charged (among other things) with independent scientific review of EPA's air quality criteria. The first and second drafts of the PM ISA were released on December 22, 2008 and July 31, 2009, respectively, for independent external peer review and public comment. These drafts were reviewed at public meetings of the CASAC PM Review Panel on April 1-2, 2009 and October 5-6, 2009, respectively. This final PM ISA has benefited from the expert comments received at the CASAC meetings and from public comments, and it has been revised accordingly.

Abstract  Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in clinical practice. Its incidence increases with age and the presence of structural heart disease. It is a major cause of stroke, especially in the elderly. Although the causes are diverse, hypertension is common. Most patients experience palpitations, but fatigue, dyspnea, and dizziness are not uncommon. Patients with an uncontrolled ventricular response during AF may occasionally develop a tachycardia-induced cardiomyopathy. There are three therapeutic goals to consider for patients with AF: rate control, maintenance of sinus rhythm, and prevention of thromboembolism. The risks and benefits of each treatment must be considered for each patient.

Abstract  Levels of ambient air pollutants, including particulate matter (PM), are often higher in low-socioeconomic status (SES) communities than in high-SES communities. Houston is the fourth largest city in the USA and is home to a large petrochemical industry, an active port, and congested roadways, which represent significant emission sources of air pollution in the region. To compare levels of air pollution between a low-SES and a high-SES community, we simultaneously collected a 7-day integrated size-fractionated PM between June 2013 and November 2013. We analyzed PM mass and elements for three particle size modes: quasi-ultrafine particles (quasi-UFP) (aerodynamic diameter <0.25 μm), accumulation mode particles (0.25-2.5 μm), and coarse mode particles (>2.5 μm). Concentrations of vanadium, nickel, manganese, and iron in the quasi-UFP mode were significantly higher in the low-SES community than in the high-SES community. In the accumulation and coarse modes, concentrations of crustal elements and barium were also significantly higher in the low-SES community compared to the high-SES community. These findings suggest that people living in the low-SES community may experience higher exposures to some toxic elements as compared to people in the high-SES community.

Abstract  The effectiveness of regulatory actions designed to improve air quality is often assessed by predicting changes in public health resulting from their implementation. Risk of premature mortality from long-term exposure to ambient air pollution is the single most important contributor to such assessments and is estimated from observational studies generally assuming a log-linear, no-threshold association between ambient concentrations and death. There has been only limited assessment of this assumption in part because of a lack of methods to estimate the shape of the exposure-response function in very large study populations. In this paper, we propose a new class of variable coefficient risk functions capable of capturing a variety of potentially non-linear associations which are suitable for health impact assessment. We construct the class by defining transformations of concentration as the product of either a linear or log-linear function of concentration multiplied by a logistic weighting function. These risk functions can be estimated using hazard regression survival models with currently available computer software and can accommodate large population-based cohorts which are increasingly being used for this purpose. We illustrate our modeling approach with two large cohort studies of long-term concentrations of ambient air pollution and mortality: the American Cancer Society Cancer Prevention Study II (CPS II) cohort and the Canadian Census Health and Environment Cohort (CanCHEC). We then estimate the number of deaths attributable to changes in fine particulate matter concentrations over the 2000 to 2010 time period in both Canada and the USA using both linear and non-linear hazard function models.

Abstract  BACKGROUND: Few studies examining the associations between long-term exposure to ambient air pollution and mortality have considered multiple pollutants when assessing changes in exposure due to residential mobility during follow-up.

OBJECTIVE: We investigated associations between cause-specific mortality and ambient concentrations of fine particulate matter (≤ 2.5 μm; PM2.5), ozone (O3), and nitrogen dioxide (NO2) in a national cohort of about 2.5 million Canadians.

METHODS: We assigned estimates of annual concentrations of these pollutants to the residential postal codes of subjects for each year during 16 years of follow-up. Historical tax data allowed us to track subjects' residential postal code annually. We estimated hazard ratios (HRs) for each pollutant separately and adjusted for the other pollutants. We also estimated the product of the three HRs as a measure of the cumulative association with mortality for several causes of death for an increment of the mean minus the 5th percentile of each pollutant: 5.0 μg/m3 for PM2.5, 9.5 ppb for O3, and 8.1 ppb for NO2.

RESULTS: PM2.5, O3, and NO2 were associated with nonaccidental and cause-specific mortality in single-pollutant models. Exposure to PM2.5 alone was not sufficient to fully characterize the toxicity of the atmospheric mix or to fully explain the risk of mortality associated with exposure to ambient pollution. Assuming additive associations, the estimated HR for nonaccidental mortality corresponding to a change in exposure from the mean to the 5th percentile for all three pollutants together was 1.075 (95% CI: 1.067, 1.084). Accounting for residential mobility had only a limited impact on the association between mortality and PM2.5 and O3, but increased associations with NO2.

CONCLUSIONS: In this large, national-level cohort, we found positive associations between several common causes of death and exposure to PM2.5, O3, and NO2.

CITATION: Crouse DL, Peters PA, Hystad P, Brook JR, van Donkelaar A, Martin RV, Villeneuve PJ, Jerrett M, Goldberg MS, Pope CA III, Brauer M, Brook RD, Robichaud A, Menard R, Burnett RT. 2015. Ambient PM2.5, O3, and NO2 exposures and associations with mortality over 16 years of follow-up in the Canadian Census Health and Environment Cohort (CanCHEC). Environ Health Perspect 123:1180-1186; http://dx.doi.org/10.1289/ehp.1409276.

Abstract  Background: Multicentric studies in Europe are required to gain knowledge on the short-term impacts of PM2.5 and PM10-2.5. We present an analysis of the short-term associations between particulate matters (PM10, PM10-2.5 and PM2.5) and mortality by causes, age-groups and seasons in nine French cities.

Methods: The associations between PM and daily mortality were investigated in each city using a generalized additive Poisson regression model for the 2000-2006 period. The percent increases in the mortality rate were estimated for a 10 mu g/m(3) increase and for an interquartile range increase in PM levels in each city, for the whole year and by season. The models also compared the PM effect observed on "non-warm" days and on "warm" days.

Results: A significant effect of PM10 (+0.8% CI 95% [0.2; 1.5] for a 10 mu g/m(3) increase) and PM2.5 (+0.7% [-0.1; 1.6]) on all-ages non-accidental mortality whole year was observed. The largest impacts were observed on all-ages cardiovascular mortality during summer for PM2.5 (+5.1% [1.8; 8.4]) and PM10-2.5 (+7.2% [2.8; 11.7]). These estimates were lowered when the model included PM2.5 and PM10-2.5. We also report a significant interaction between warm days and PM. Adjusting PM on ozone did not modify the results for the whole year, but decreased the estimates for summer, when a high correlation is observed between these pollutants.

Conclusions: Our results confirm the short-term impacts of PM10 on mortality, even at concentrations complying with the European annual regulation. They underline the short-term impacts of PM2.5 and PM10-2.5 and call for the setting of regulation values for these PM indicators. (C) 2014 Elsevier Ltd. All rights reserved.

Abstract  BACKGROUND: Evidence on health effects of ultrafine particles (UFP) is still limited as they are usually not monitored routinely. The few epidemiological studies on UFP and (cause-specific) mortality so far have reported inconsistent results.

OBJECTIVES: The main objective of the UFIREG project was to investigate the short-term associations between UFP and fine particulate matter (PM)<2.5μm (PM2.5) and daily (cause-specific) mortality in five European Cities. We also examined the effects of PM<10μm (PM10) and coarse particles (PM2.5-10).

METHODS: UFP (20-100nm), PM and meteorological data were measured in Dresden and Augsburg (Germany), Prague (Czech Republic), Ljubljana (Slovenia) and Chernivtsi (Ukraine). Daily counts of natural and cardio-respiratory mortality were collected for all five cities. Depending on data availability, the following study periods were chosen: Augsburg and Dresden 2011-2012, Ljubljana and Prague 2012-2013, Chernivtsi 2013-March 2014. The associations between air pollutants and health outcomes were assessed using confounder-adjusted Poisson regression models examining single (lag 0-lag 5) and cumulative lags (lag 0-1, lag 2-5, and lag 0-5). City-specific estimates were pooled using meta-analyses methods.

RESULTS: Results indicated a delayed and prolonged association between UFP and respiratory mortality (9.9% [95%-confidence interval: -6.3%; 28.8%] increase in association with a 6-day average increase of 2750particles/cm(3) (average interquartile range across all cities)). Cardiovascular mortality increased by 3.0% [-2.7%; 9.1%] and 4.1% [0.4%; 8.0%] in association with a 12.4μg/m(3) and 4.7μg/m(3) increase in the PM2.5- and PM2.5-10-averages of lag 2-5.

CONCLUSIONS: We observed positive but not statistically significant associations between prolonged exposures to UFP and respiratory mortality, which were independent of particle mass exposures. Further multi-centre studies are needed investigating several years to produce more precise estimates on health effects of UFP.

Abstract  BACKGROUND: Particulate matter (PM) exposure may directly affect the pulmonary vasculature. While the pulmonary vasculature is not easily measurable, differential associations for right ventricular (RV) and left ventricular (LV) mass may provide an indirect assessment of pulmonary vascular damage.

OBJECTIVES: To test whether long-term exposure to PM <2.5μm (PM2.5) is associated with greater RV mass and RV mass/end-diastolic volume ratio relative to the LV.

METHODS: The Multi-Ethnic Study of Atherosclerosis performed cardiac magnetic resonance (CMR) imaging among participants 45-84 years old without clinical cardiovascular disease in 2000-02 in six U.S. cities. A fine-scale spatiotemporal model estimated ambient PM2.5 exposure in the year before CMR; individually-weighted estimates account for indoor exposure to ambient PM2.5. Linear regression models were adjusted for demographics, anthropometrics, smoking status, cardiac risk factors and LV parameters, with additional adjustment for city.

RESULTS: The 4,041 included participants were a mean of 61.5 years old and 47% were never smokers. The mean ambient PM2.5 was 16.4 μg/m(3) and individually-weighted PM2.5 was 11.0 μg/m(3). PM2.5 exposure was associated with a greater RV mass (ambient: 0.11 g per 5 μg/m(3), 95% CI: -0.05, 0.27; individually-weighted: 0.20 g per 5 μg/m(3), 95% CI: 0.04, 0.36) and a greater RV mass/end-diastolic volume ratio conditional on LV parameters. City-adjusted results for RV mass were of greater magnitude and statistically significant for both measures of PM2.5, while those for RV mass/end-diastolic volume ratio were attenuated.

CONCLUSIONS: Long-term PM2.5 exposures were associated with greater RV mass and RV mass/end-diastolic volume ratio conditional on the LV, however additional adjustment for city attenuated the RV mass/end-diastolic volume findings. These findings suggest that PM2.5 exposure may be associated with subclinical cardiopulmonary differences in this general population sample.

Abstract  BACKGROUND: Ambient particulate matter has been shown to be associated with declining human health, although the association between fine particulate matter (PM2.5) and stroke is uncertain.

METHODS: We utilized satellite-derived measures of PM2.5 to examine the association between exposure and stroke in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. We used a time-stratified case-crossover design, with exposure lags of 1 day, 2 days, and 3 days. We examined all strokes, as well as ischemic and hemorrhagic strokes separately.

RESULTS: Among 30,239 participants in the REGARDS study, 746 incident events were observed: 72 hemorrhagic, 617 ischemic, and 57 of unknown type. Participants exposed to higher levels of PM2.5 more often resided in urban areas compared to rural, and in the southeastern United States. After adjustment for temperature and relative humidity, no association was observed between PM2.5 exposure and stroke, regardless of the lag (1-day lag OR = .99, 95% CI: .83-1.19; 2-day lag OR = .95, 95% CI: .80-1.14; 3-day lag OR = .95, 95% CI = .79-1.13). Similar results were observed for the stroke subtypes.

CONCLUSIONS: In this large cohort of African-Americans and whites, no association was observed between PM2.5 and stroke. The ability to examine this association with a large number of outcomes and by stroke subtype helps fill a gap in the literature examining the association between PM2.5 and stroke.

Abstract  In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models.