Abstract  The authors investigated whether short-term effects of fine particulate matter with an aerodynamic diameter ≤2.5 µm (PM2.5) on risk of cardiovascular and respiratory hospitalizations among the elderly varied by region and season in 202 US counties for 1999–2005. They fit 3 types of time-series models to provide evidence for 1) consistent particulate matter effects across the year, 2) different particulate matter effects by season, and 3) smoothly varying particulate matter effects throughout the year. The authors found statistically significant evidence of seasonal and regional variation in estimates of particulate matter effect. Respiratory disease effect estimates were highest in winter, with a 1.05% (95% posterior interval: 0.29, 1.82) increase in hospitalizations per 10-µg/m3 increase in same-day PM2.5. Cardiovascular diseases estimates were also highest in winter, with a 1.49% (95% confidence interval: 1.09, 1.89) increase in hospitalizations per 10-µg/m3 increase in same-day PM2.5, with associations also observed in other seasons. The strongest evidence of a relation between PM2.5 and hospitalizations was in the Northeast for both respiratory and cardiovascular diseases. Heterogeneity of PM2.5 effects on hospitalizations may reflect seasonal and regional differences in emissions and in particles’ chemical constituents. Results can help guide development of hypotheses and further epidemiologic studies on potential heterogeneity in the toxicity of constituents of the particulate matter mixture.

Abstract  Studies suggest that long-term chronic exposure to fine particulate matter air pollution can increase lung cancer mortality. We analyzed the association between long term PM2.5 and ozone exposure and mortality due to lung cancer, ischemic heart disease, and chronic obstructive pulmonary disease, accounting for geographic location, socioeconomic status, and residential mobility. Subjects in the 1991 Canadian Census Health and Environment Cohort (CanCHEC) were followed for 20years, and assigned to regions across Canada based on spatial synoptic classification weather types. Hazard ratios (HR) for mortality, were related to PM2.5 and ozone using Cox proportional hazards survival models, adjusting for socioeconomic characteristics and individual confounders. An increase of 10μg/m3 in long term PM2.5 exposure resulted in an HR for lung cancer mortality of 1.26 (95% CI 1.04, 1.53); the inclusion in the model of SSC zone as a stratum increased the risk estimate to HR 1.29 (95% CI 1.06, 1.57). After adjusting for ozone, HRs increased to 1.49 (95% CI 1.23, 1.88), and HR 1.54 (95% CI 1.27, 1.87), with and without zone as a model stratum. HRs for ischemic heart disease fell from 1.25 (95% CI 1.21, 1.29) for exposure to PM2.5, to 1.13 (95% CI 1.08, 1.19) when PM2.5 was adjusted for ozone. For COPD, the 95% confidence limits included 1.0 when climate zone was included in the model. HRs for all causes of death showed spatial differences when compared to zone 3, the most populated climate zone. Exposure to PM2.5 was related to an increased risk of mortality from lung cancer, and both ozone and PM2.5 exposure were related to risk of mortality from ischemic heart disease, and the risk varied spatially by climate zone.

Abstract  INTRODUCTION: This report provides a summary of major findings and key conclusions supported by a Health Effects Institute grant aimed at "Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Pollution." Our study was designed to advance four critical areas of inquiry and methods development.

METHODS: First, our work focused on predicting short- and long-term exposures to ambient PM2.5 mass (particulate matter ≤ 2.5μm in aerodynamic diameter) and ozone (O3) at high spatial resolution (1 km × 1 km) for the continental United States during the period 2000-2012 and linking these predictions to health data. Second, we developed new causal inference methods for exposure-response (ER) that account for exposure error and adjust for measured confounders. We applied these methods to data from the New England region. Third, we applied standard regression methods using Medicare claims data to estimate health effects that are associated with short- and long-term exposure to low levels of ambient air pollution. We conducted sensitivity analyses to assess potential confounding bias due to lack of extensive information on behavioral risk factors in the Medicare population using the Medicare Current Beneficiary Survey (MCBS) (nationally representative sample of approximately 15,000 Medicare enrollees per year), which includes abundant data on individual-level risk factors including smoking. Finally, we have begun developing tools for reproducible research - including approaches for data sharing, record linkage, and statistical software.

RESULTS: Our HEI-funded work has supported an extensive portfolio of analysis and the development of statistical methods that can be used to robustly understand the health effects of long- and short-term exposure to low levels of ambient air pollution. This report provides a high-level overview of statistical methods, data analysis, and key findings, as grouped into the following four areas: (1) Exposure assessment and data access; (2) Epidemiological studies of ambient exposures to air pollution at low levels; (3) Methodological contributions in causal inference; and (4) Open science research data platform.

CONCLUSION: Our body of work, advanced by HEI, lends extensive evidence that short- and long-term exposure to PM2.5 and O3 is harmful to human health, increasing the risks of hospitalization and death, even at levels that are well below the National Ambient Air Quality Standards (NAAQS).

Abstract  Determining how associations between ambient air pollution and health vary by specific outcome is important for developing public health interventions. We estimated associations between twelve ambient air pollutants of both primary (e.g. nitrogen oxides) and secondary (e.g. ozone and sulfate) origin and cardiorespiratory emergency department (ED) visits for 8 specific outcomes in five U.S. cities including Atlanta, GA; Birmingham, AL; Dallas, TX; Pittsburgh, PA; St. Louis, MO. For each city, we fitted overdispersed Poisson time-series models to estimate associations between each pollutant and specific outcome. To estimate multicity and posterior city-specific associations, we developed a Bayesian multicity multi-outcome (MCM) model that pools information across cities using data from all specific outcomes. We fitted single pollutant models as well as models with multipollutant components using a two-stage chemical mixtures approach. Posterior city-specific associations from the MCM models were somewhat attenuated, with smaller standard errors, compared to associations from time-series regression models. We found positive associations of both primary and secondary pollutants with respiratory disease ED visits. There was some indication that primary pollutants, particularly nitrogen oxides, were also associated with cardiovascular disease ED visits. Bayesian models can help to synthesize findings across multiple outcomes and cities by providing posterior city-specific associations building on variation and similarities across the multiple sources of available information.

Abstract  Spatiotemporal characterization of ambient air pollutant concentrations is increasingly relying on the combination of observations and air quality models to provide well-constrained, spatially and temporally complete pollutant concentration fields. Air quality models, in particular, are attractive, as they characterize the emissions, meteorological, and physiochemical process linkages explicitly while providing continuous spatial structure. However, such modeling is computationally intensive and has biases. The limitations of spatially sparse and temporally incomplete observations can be overcome by blending the data with estimates from a physically and chemically coherent model, driven by emissions and meteorological inputs. We recently developed a data fusion method that blends ambient ground observations and chemical transport -modeled (CTM) data to estimate daily, spatially resolved pollutant concentrations and associated correlations. In this study, we assess the ability of the data fusion method to produce daily metrics (i.e., 1-hr max, 8-hr max, and 24-hr average) of ambient air pollution that capture spatiotemporal air pollution trends for 12 pollutants (CO, NO2, NOx, O-3, SO2, PM10, PM2.5, and five PM2.5 components) across five metropolitan areas (Atlanta, Birmingham, Dallas, Pittsburgh, and St. Louis), from 2002 to 2008.

Three sets of comparisons are performed: (1) the CTM concentrations are evaluated for each pollutant and metropolitan domain, (2) the data fusion concentrations are compared with the monitor data, (3) a comprehensive cross-validation analysis against observed data evaluates the quality of the data fusion model simulations across multiple metropolitan domains. The resulting daily spatial field estimates of air pollutant concentrations and uncertainties are not only consistent with observations, emissions, and meteorology, but substantially improve CTM-derived results for nearly all pollutants and all cities, with the exception of NO2 for Birmingham. The greatest improvements occur for O-3 and PM2.5. Squared spatiotemporal correlation coefficients range between simulations and observations determined using cross-validation across all cities for air pollutants of secondary and mixed origins are R-2 = 0.88-0.93 (O-3), 0.81-0.89 (SO4), 0.67-0.83 (PM2.5), 0.52-0.72 (NO3), 0.43-0.80 (NH4), 0.32-0.51 (OC), and 0.14-0.71 (PM10).

Results for relatively homogeneous pollutants of secondary origin, tend to be better than those for more spatially heterogeneous (larger spatial gradients) pollutants of primary origin (NOx, CO, SO2 and EC). Generally, background concentrations and spatial concentration gradients reflect interurban airshed complexity and the effects of regional transport, whereas daily spatial pattern variability shows intraurban consistency in the fused data. With sufficiently high CTM spatial resolution, traffic-related pollutants exhibit gradual concentration gradients that peak toward the urban centers. Ambient pollutant concentration uncertainty estimates for the fused data are both more accurate and smaller than those for either the observations or the model simulations alone. (C) 2017 Elsevier Ltd. All rights reserved.

Abstract  BACKGROUND: Studies have reported associations between long-term air pollution exposures and cardiovascular mortality. The biological mechanisms connecting them remain uncertain.

METHODS: We examined associations of fine particles (PM2.5) and ozone with serum markers of cardiovascular disease risk in a cohort of midlife women. We obtained information from women enrolled at six sites in the multi-ethnic, longitudinal Study of Women's Health Across the Nation, including repeated measurements of high-sensitivity C-reactive protein (hs-CRP), fibrinogen, tissue-type plasminogen activator antigen (tPA-ag), plasminogen activator inhibitor Type 1 (PAI-1), and Factor VIIc (Factor VII coagulant activity). We obtained residence-proximate PM2.5 and ozone monitoring data for a maximum five annual visits, calculating prior year, six-month, one-month, and one-day exposures and their relations to serum markers using longitudinal mixed models.

RESULTS: For the 2,086 women studied from 1999 through 2004,PM2.5 exposures were associated with all blood markers except Factor VIIc after adjusting for age, race/ethnicity, education, site, body mass index, smoking, and recent alcohol use. Adjusted associations were of the strongest for prior year exposures for hs-CRP (21% increase per 10 µg/m PM2.5, 95% CI: 6.6, 37), tPA-ag (8.6%, 95% CI: 1.8, 16), and PAI-1 (35%, 95% CI: 19, 53). An association was also observed between year prior ozone exposure and Factor VIIc (5.7% increase per 10 ppb ozone, 95% CI: 2.9, 8.5).

CONCLUSIONS: Our findings suggest that prior year exposures to PM2.5 and ozone are associated with adverse effects on inflammatory and hemostatic pathways for cardiovascular outcomes in midlife women.

Abstract  OBJECTIVES: To quantify nationwide disparities in the location of particulate matter (PM)-emitting facilities by the characteristics of the surrounding residential population and to illustrate various spatial scales at which to consider such disparities.

METHODS: We assigned facilities emitting PM in the 2011 National Emissions Inventory to nearby block groups across the 2009 to 2013 American Community Survey population. We calculated the burden from these emissions for racial/ethnic groups and by poverty status. We quantified disparities nationally and for each state and county in the country.

RESULTS: For PM of 2.5 micrometers in diameter or less, those in poverty had 1.35 times higher burden than did the overall population, and non-Whites had 1.28 times higher burden. Blacks, specifically, had 1.54 times higher burden than did the overall population. These patterns were relatively unaffected by sensitivity analyses, and disparities held not only nationally but within most states and counties as well.

CONCLUSIONS: Disparities in burden from PM-emitting facilities exist at multiple geographic scales. Disparities for Blacks are more pronounced than are disparities on the basis of poverty status. Strictly socioeconomic considerations may be insufficient to reduce PM burdens equitably across populations. (Am J Public Health. Published online ahead of print February 22, 2018: e1-e6. doi:10.2105/AJPH.2017.304297).

Abstract  BACKGROUND: Multi-city population-based epidemiological studies have observed heterogeneity between city-specific fine particulate matter (PM2.5)-mortality effect estimates. These studies typically use ambient monitoring data as a surrogate for exposure leading to potential exposure misclassification. The level of exposure misclassification can differ by city affecting the observed health effect estimate.

METHODS: The objective of this analysis is to evaluate whether previously developed residential infiltration-based city clusters can explain city-to-city heterogeneity in PM2.5 mortality risk estimates. In a prior paper 94 cities were clustered based on residential infiltration factors (e.g. home age/size, prevalence of air conditioning (AC)), resulting in 5 clusters. For this analysis, the association between PM2.5 and all-cause mortality was first determined in 77 cities across the United States for 2001-2005. Next, a second stage analysis was conducted evaluating the influence of cluster assignment on heterogeneity in the risk estimates.

RESULTS: Associations between a 2-day (lag 0-1 days) moving average of PM2.5 concentrations and non-accidental mortality were determined for each city. Estimated effects ranged from -3.2 to 5.1% with a pooled estimate of 0.33% (95% CI: 0.13, 0.53) increase in mortality per 10 μg/m(3) increase in PM2.5. The second stage analysis determined that cluster assignment was marginally significant in explaining the city-to-city heterogeneity. The health effects estimates in cities with older, smaller homes with less AC (Cluster 1) and cities with newer, smaller homes with a large prevalence of AC (Cluster 3) were significantly lower than the cluster consisting of cities with older, larger homes with a small percentage of AC.

CONCLUSIONS: This is the first study that attempted to examine whether multiple exposure factors could explain the heterogeneity in PM2.5-mortality associations. The results of this study were found to explain a small portion (6%) of this heterogeneity.

Abstract  Background: Remote sensing (RS) is increasingly used for exposure assessment in epidemiological and burden of disease studies, including those investigating whether chronic exposure to ambient fine particulate matter (PM2.5) is associated with mortality. Objectives: To compare relative risk estimates of mortality from diseases of the circulatory system for PM2.5 modeled from RS with that for PM2.5 modeled using ground-level information. Methods: We geocoded the baseline residence of 668,629 American Cancer Society Cancer Prevention Study II (CPS-II) cohort participants followed from 1982 to 2004 and assigned PM2.5 levels to all participants using seven different exposure models. Most of the exposure models were averaged for the years 2002-2004, while one RS estimate was for a longer, contemporaneous period. We used Cox proportional hazards regression to estimate relative risks (RR) for the association of PM2.5 with circulatory mortality and ischemic heart disease. Results: Estimates of mortality risk differed among exposure models. The smallest relative risk was observed for the RS estimates that excluded ground-based monitors for circulatory deaths (RR = 1.02 (95% confidence interval (CI): 1.00-1.04 per 10 µg/m3 increment in PM2.5). The largest relative risk was observed for the land use regression model that included traffic information (RR = 1.14, 95% CI: 1.11-1.17 per 10 µg/m3 increment in PM2.5). Conclusions: We found significant associations between PM2.5 and mortality in every model; however, relative risks estimated from exposure models using ground-based information were generally larger than those estimated with RS alone.

Abstract  BACKGROUND: Long-term exposure to ambient particulate matter (PM) has been previously linked with higher risk of cardiovascular events. This association may be mediated, at least partly, by increasing the risk of incident hypertension, a key determinant of cardiovascular risk. However, whether long-term exposure to PM is associated with incident hypertension remains unclear.

METHODS: Using national geostatistical models incorporating geographic covariates and spatial smoothing, we estimated annual average concentrations of residential fine (PM2.5), respirable (PM10), and course (PM10-2.5) fractions of particulate matter among 44,255 post-menopausal women free of hypertension enrolled in the Women's Health Initiative (WHI) clinical trials. We used time-varying Cox proportional hazards models to evaluate the association between long-term average residential pollutant concentrations and incident hypertension, adjusting for potential confounding by sociodemographic factors, medical history, neighborhood socioeconomic measures, WHI study clinical site, clinical trial, and randomization arm.

RESULTS: During 298,383 person-years of follow-up, 14,511 participants developed incident hypertension. The adjusted hazard ratios per interquartile range (IQR) increase in PM2.5, PM10, and PM10-2.5 were 1.13 (95% CI: 1.08, 1.17), 1.06 (1.03, 1.10), and 1.01 (95% CI: 0.97, 1.04), respectively. Statistically significant concentration-response relationships were identified for PM2.5 and PM10 fractions. The association between PM2.5 and hypertension was more pronounced among non-white participants and those residing in the Northeastern United States.

CONCLUSIONS: In this cohort of post-menopausal women, ambient fine and respirable particulate matter exposures were associated with higher incidence rates of hypertension. These results suggest that particulate matter may be an important modifiable risk factor for hypertension.

Abstract  We describe a remote sensing and geographic information system (GIS)-based study that has three objectives: (1) characterize fine particulate matter (PM2.5), insolation and land surface temperature (LST) using NASA satellite observations, Environmental Protection Agency (EPA) ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.

Abstract  BACKGROUND: Despite dramatic air quality improvement in the United States over the past decades, recent years have brought renewed scrutiny and uncertainty surrounding the effectiveness of specific regulatory programs for continuing to improve air quality and public health outcomes.

METHODS: We employ causal inference methods and a spatial hierarchical regression model to characterize the extent to which a designation of "nonattainment" with the 1997 National Ambient Air Quality Standard for ambient fine particulate matter (PM2.5) in 2005 causally affected ambient PM2.5 and health outcomes among over 10 million Medicare beneficiaries in the Eastern US in 2009-2012.

RESULTS: We found that, on average across all retained study locations, reductions in ambient PM2.5 and Medicare health outcomes could not be conclusively attributed to the nonattainment designations against the backdrop of other regional strategies that impacted the entire Eastern United States. A more targeted principal stratification analysis indicates substantial health impacts of the nonattainment designations among the subset of areas where the designations are estimated to have actually reduced ambient PM2.5 beyond levels achieved by regional measures, with noteworthy reductions in all-cause mortality, chronic obstructive pulmonary disorder, heart failure, ischemic heart disease, and respiratory tract infections.

DISCUSSION: These findings provide targeted evidence of the effectiveness of local control measures following nonattainment designations for the 1997 PM2.5 air quality standard.

Abstract  BACKGROUND: Few studies have investigated the independent health effects of different size fractions of particulate matter (PM), in multiple locations, especially in Europe. OBJECTIVES: We estimated the short-term effects of PM with aerodynamic diameter less than 10μm (PM10), less than 2.5μm (PM2.5), and between 2.5 and 10μm (PM2.5-10) on all-cause, cardiovascular and respiratory mortality in 10 European Mediterranean metropolitan areas within the MED-PARTICLES project. METHODSs: We analyzed data from each city using Poisson regression models, and combined city-specific estimates to derive overall effect estimates. We evaluated the sensitivity of our estimates to co-pollutant exposures and city-specific model choice, and investigated effect modification by age, sex, and season. We applied distributed lag and threshold models to investigate temporal patterns of associations. RESULTS: A 10-μg/m(3) increase in PM2.5 was associated with a 0.55% (95% CI: 0.27, 0.84%) increase in all-cause mortality (0-1 day cumulative lag), and a 1.91% increase (95%CI: 0.71, 3.12%) in respiratory mortality (0-5 day lag). In general, associations were stronger for cardiovascular and respiratory mortality than all-cause mortality, during warm versus cold months, and among those ≥ 75 versus <75 years of age. Associations with PM2.5-10 were positive but not statistically significant in most analyses, while associations with PM10 seemed to be driven by PM2.5 CONCLUSIONS: We found evidence of adverse effects of PM2.5 on mortality outcomes in the European Mediterranean region. Associations with PM2.5-10 were positive but smaller in magnitude. Associations were stronger for respiratory mortality when cumulative exposures were lagged over 0-5 days, and were modified by season and age.

Abstract  BACKGROUND: Many studies have reported associations between ambient particulate matter (PM) and adverse health effects, focused on either short-term (acute) or long-term (chronic) PM exposures. For chronic effects, the studied cohorts have rarely been representative of the population. We present a novel exposure model combining satellite aerosol optical depth and land-use data to investigate both the long- and short-term effects of PM2.5 exposures on population mortality in Massachusetts, United States, for the years 2000-2008.

METHODS: All deaths were geocoded. We performed two separate analyses: a time-series analysis (for short-term exposure) where counts in each geographic grid cell were regressed against cell-specific short-term PM2.5 exposure, temperature, socioeconomic data, lung cancer rates (as a surrogate for smoking), and a spline of time (to control for season and trends). In addition, for long-term exposure, we performed a relative incidence analysis using two long-term exposure metrics: regional 10 × 10 km PM2.5 predictions and local deviations from the cell average based on land use within 50 m of the residence. We tested whether these predicted the proportion of deaths from PM-related causes (cardiovascular and respiratory diseases).

RESULTS: For short-term exposure, we found that for every 10-µg/m increase in PM 2.5 exposure there was a 2.8% increase in PM-related mortality (95% confidence interval [CI] = 2.0-3.5). For the long-term exposure at the grid cell level, we found an odds ratio (OR) for every 10-µg/m increase in long-term PM2.5 exposure of 1.6 (CI = 1.5-1.8) for particle-related diseases. Local PM2.5 had an OR of 1.4 (CI = 1.3-1.5), which was independent of and additive to the grid cell effect.

CONCLUSIONS: We have developed a novel PM2.5 exposure model based on remote sensing data to assess both short- and long-term human exposures. Our approach allows us to gain spatial resolution in acute effects and an assessment of long-term effects in the entire population rather than a selective sample from urban locations.

Abstract  INTRODUCTION: Chronic environmental exposure to particulate matter <2.5μm in diameter (PM2.5) has been associated with cardiovascular disease; however, the effect of air pollution on myocardial infarction (MI) survivors is not clear. We studied the association of chronic exposure to PM2.5 with death and recurrent cardiovascular events in MI survivors.

METHODS: Consecutive patients aged ≤65years admitted to all medical centers in central Israel after first-MI in 1992-1993 were followed through 2005 for cardiovascular events and 2011 for survival. Data on sociodemographic and prognostic factors were collected at baseline and during follow-up. Residential exposure to PM2.5 was estimated for each patient based on data recorded at air quality monitoring stations. Cox and Andersen-Gill proportional hazards models were used to study the pollution-outcome association.

RESULTS: Among the 1120 patients, 469 (41.9%) died and 541 (48.3%) experienced one or more recurrent cardiovascular event. The adjusted hazard ratios associated with a 10μg/m(3) increase in PM2.5 exposure were 1.3 (95% CI 0.8-2.1) for death and 1.5 (95% CI 1.1-1.9) for multiple recurrences of cardiovascular events (MI, heart failure and stroke).

CONCLUSION: When adjustment for socio-demographic factors is performed, cumulative chronic exposure to PM2.5 is positively associated with recurrence of cardiovascular events in patients after a first MI.

Abstract  BACKGROUND: Many studies report significant associations between PM(2.5) (particulate matter <2.5 micrometers) and hospital admissions. These studies mostly rely on a limited number of monitors which introduces exposure error, and excludes rural and suburban populations from locations where monitors are not available, reducing generalizability and potentially creating selection bias.

METHODS: Using prediction models developed by our group, daily PM(2.5) exposure was estimated across the Mid-Atlantic (Washington D.C., and the states of Delaware, Maryland, New Jersey, Pennsylvania, Virginia, New York and West Virginia). We then investigated the short-term effects of PM(2.5)exposures on emergency hospital admissions of the elderly in the Mid-Atlantic region.We performed case-crossover analysis for each admission type, matching on day of the week, month and year and defined the hazard period as lag01 (a moving average of day of admission exposure and previous day exposure).

RESULTS: We observed associations between short-term exposure to PM(2.5) and hospitalization for all outcomes examined. For example, for every 10-µg/m(3) increase in short-term PM(2.5) there was a 2.2% increase in respiratory diseases admissions (95% CI = 1.9 to 2.6), and a 0.78% increase in cardiovascular disease (CVD) admission rate (95% CI = 0.5 to 1.0). We found differences in risk for CVD admissions between people living in rural and urban areas. For every10-µg/m(3) increase in PM(2.5) exposure in the 'rural' group there was a 1.0% increase (95% CI = 0.6 to 1.5), while for the 'urban' group the increase was 0.7% (95% CI = 0.4 to 1.0).

CONCLUSIONS: Our findings showed that PM(2.5) exposure was associated with hospital admissions for all respiratory, cardio vascular disease, stroke, ischemic heart disease and chronic obstructive pulmonary disease admissions. In addition, we demonstrate that our AOD (Aerosol Optical Depth) based exposure models can be successfully applied to epidemiological studies investigating the health effects of short-term exposures to PM(2.5).

Abstract  Features of the environment may modify the effect of particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) on health. Therefore, we investigated how neighborhood sociodemographic and land-use characteristics may modify the association between PM2.5 and cardiovascular mortality. We obtained residence-level geocoded cardiovascular mortality cases from the Massachusetts Department of Public Health (n = 179,986), and PM2.5 predictions from a satellite-based model (2001⁻2011). We appended census block group-level information on sociodemographic factors and walkability, and calculated neighborhood greenness within a 250 m buffer surrounding each residence. We found a 2.54% (1.34%; 3.74%) increase in cardiovascular mortality associated with a 10 µg/m³ increase in two-day average PM2.5. Walkability or greenness did not modify the association. However, when stratifying by neighborhood sociodemographic characteristics, smaller PM2.5 effects were observed in greener areas only among cases who resided in neighborhoods with a higher population density and lower percentages of white residents or residents with a high school diploma. In conclusion, the PM2.5 effects on cardiovascular mortality were attenuated by higher greenness only in areas with sociodemographic features that are highly correlated with lower socioeconomic status. Previous evidence suggests health benefits linked to neighborhood greenness may be stronger among lower socioeconomic groups. Attenuation of the PM2.5⁻mortality relationship due to greenness may explain some of this evidence.

Abstract  BACKGROUND: Previous studies reported triggering of acute cardiovascular events by short-term increasedPM2.5 concentrations. From 2007 to 2013, national and New York state air quality policies and economic influences resulted in reduced concentrations of PM2.5 and other pollutants across the state. We estimated the rate of cardiovascular hospital admissions associated with increased PM2.5 concentrations in the previous 1-7 days, and evaluated whether they differed before (2005-2007), during (2008-2013), and after these concentration changes (2014-2016).

METHODS: Using the Statewide Planning and Research Cooperative System (SPARCS) database, we retained all hospital admissions with a primary diagnosis of nine cardiovascular disease (CVD) subtypes, for residents living within 15 miles of PM2.5 monitoring sites in Buffalo, Rochester, Albany, Queens, Bronx, and Manhattan from 2005 to 2016 (N = 1,922,918). We used a case-crossover design and conditional logistic regression to estimate the admission rate for total CVD, and nine specific subtypes, associated with increased PM2.5 concentrations. RESULTS: Interquartile range (IQR) increases in PM2.5 on the same and previous 6 days were associated with 0.6%-1.2% increases in CVD admission rate (2005-2016). There were similar patterns for cardiac arrhythmia, ischemic stroke, congestive heart failure, ischemic heart disease (IHD), and myocardial infarction (MI). Ambient PM2.5 concentrations and annual total CVD admission rates decreased across the period. However, the excess rate of IHD admissions associated with each IQR increase in PM2.5 in previous 2 days was larger in the after period (2.8%; 95%CI = 1.5%-4.0%) than in the during (0.6%; 95%CI = 0.0%-1.2%) or before periods (0.8%; 95%CI = 0.2%-1.3%), with similar patterns for total CVD and MI, but not other subtypes. CONCLUSIONS: While pollutant concentrations and CVD admission rates decreased after emission changes, the same PM2.5 mass was associated with a higher rate of ischemic heart disease events. Future work should confirm these findings in another population, and investigate whether specific PM components and/or sources trigger IHD events.

Abstract  Accurate data concerning historical fine particulate matter (PM2.5) concentrations are needed to assess long-term changes in exposure and associated health risks. We estimated historical PM2.5 concentrations over North America from 1981 to 2016 for the first time by combining chemical transport modeling, satellite remote sensing, and ground-based measurements. We constrained and evaluated our estimates with direct ground-based PM2.5 measurements when available and otherwise with historical estimates of PM2.5 from PM10 measurements or total suspended particle (TSP) measurements. The estimated PM2.5 concentrations were generally consistent with direct ground-based PM2.5 measurements over their duration from 1988 onward ( R2 = 0.6 to 0.85) and to a lesser extent with PM2.5 inferred from PM10 measurements from 1985 to 1998 ( R2 = 0.5 to 0.6). The collocated comparison of the trends of population-weighted annual average PM2.5 from our estimates and ground-based measurements was highly consistent (RMSD = 0.66 μg m-3). The population-weighted annual average PM2.5 over North America decreased from 22 ± 6.4 μg m-3 in 1981, to 12 ± 3.2 μg m-3 in 1998, and to 7.9 ± 2.1 μg m-3 in 2016, with an overall trend of -0.33 μg m-3 yr-1 (95% CI: -0.35, -0.31).

Abstract  BACKGROUND: Ecological evidence suggests that exposure to air pollution affects coronavirus disease 2019 (COVID-19) outcomes. However, no individual-level study has confirmed the association to date.

METHODS: We identified COVID-19 patients diagnosed at the University of Cincinnati hospitals and clinics and estimated particulate matter ≤2.5 μm (PM2.5) exposure over a 10-year period (2008-2017) at their residential zip codes. We used logistic regression to evaluate the association between PM2.5 exposure and hospitalizations for COVID-19, adjusting for socioeconomic characteristics and comorbidities.

RESULTS: Among the 1128 patients included in our study, the mean (standard deviation) PM2.5 was 11.34 (0.70) μg/m3 for the 10-year average exposure and 13.83 (1.03) μg/m3 for the 10-year maximal exposures. The association between long-term PM2.5 exposure and hospitalization for COVID-19 was contingent upon having pre-existing asthma or chronic obstructive pulmonary (COPD) (Pinteraction = 0.030 for average PM2.5 and Pinteraction = 0.001 for maximal PM2.5). In COVID-19 patients with asthma or COPD, the odds of hospitalization were 62% higher with 1 μg/m3 increment in 10-year average PM2.5 (odds ratio [OR]: 1.62, 95% confidence interval [CI]: 1.00-2.64) and 65% higher with 1 μg/m3 increase in 10-year maximal PM2.5 levels (OR: 1.65, 95% CI: 1.16-2.35). However, among COVID-19 patients without asthma or COPD, PM2.5 exposure was not associated with higher hospitalizations (OR: 0.84, 95% CI: 0.65-1.09 for average PM2.5 and OR: 0.78, 95% CI: 0.65-0.95 for maximal PM2.5).

CONCLUSIONS: Long-term exposure to PM2.5 is associated with higher odds of hospitalization in COVID-19 patients with pre-existing asthma or COPD.

Abstract  BACKGROUND: Studies have reported that ambient air pollution is associated with an increased risk of developing or dying from coronavirus-2 (COVID-19). Methodological approaches to investigate the health impacts of air pollution on epidemics should differ from those used for chronic diseases, but the methods used in these studies have not been appraised critically.

OBJECTIVES: Our study aimed to identify and critique the methodological approaches of studies of air pollution on infections and mortality due to COVID-19 and to identify and critique the methodological approaches of similar studies concerning severe acute respiratory syndrome (SARS).

METHODS: Published and unpublished papers of associations between air pollution and developing or dying from COVID-19 or SARS that were reported as of 10 May 2020 were identified through electronic databases, internet searches, and other sources.

RESULTS: All six COVID-19 studies and two of three SARS studies reported positive associations. Two were time series studies that estimated associations between daily changes in air pollution, one was a cohort that assessed associations between air pollution and the secondary spread of SARS, and six were ecological studies that used area-wide exposures and outcomes. Common shortcomings included possible cross-level bias in ecological studies, underreporting of health outcomes, using grouped data, the lack of highly spatially resolved air pollution measures, inadequate control for confounding and evaluation of effect modification, not accounting for regional variations in the timing of outbreaks' temporal changes in at-risk populations, and not accounting for nonindependence of outcomes.

DISCUSSION: Studies of air pollution and novel coronaviruses have relied mainly on ecological measures of exposures and outcomes and are susceptible to important sources of bias. Although longitudinal studies with individual-level data may be imperfect, they are needed to adequately address this topic. The complexities involved in these types of studies underscore the need for careful design and for peer review. https://doi.org/10.1289/EHP7411.

Abstract  BACKGROUND: Studies have long associated [Formula: see text] with daily mortality, but few applied causal-modeling methods, or at low exposures. Short-term exposure to [Formula: see text], a marker of local traffic, has also been associated with mortality but is less studied. We previously found a causal effect between local air pollution and mortality in Boston.

OBJECTIVES: We aimed to estimate the causal effects of local pollution, [Formula: see text], and [Formula: see text] on mortality in 135 U.S. cities.

METHODS: We used three methods which, under different assumptions, provide causal marginal estimates of effect: a marginal structural model, an instrumental variable analysis, and a negative exposure control. The instrumental approach used planetary boundary layer, wind speed, and air pressure as instruments for concentrations of local pollutants; the marginal structural model separated the effects of [Formula: see text] from the effects of [Formula: see text], and the negative exposure control provided protection against unmeasured confounders.

RESULTS: In 7.3 million deaths, the instrumental approach estimated that mortality increased 1.5% [95% confidence interval (CI): 1.1%, 2.0%] per [Formula: see text] increase in local pollution indexed as [Formula: see text]. The negative control exposure was not associated with mortality. Restricting our analysis to days with [Formula: see text] below [Formula: see text], we found a 1.70% (95% CI 1.11%, 2.29%) increase. With marginal structural models, we found positive significant increases in deaths with both [Formula: see text] and [Formula: see text]. On days with [Formula: see text] below [Formula: see text], we found a 0.83% (95% CI 0.39%, 1.27%) increase. Including negative exposure controls changed estimates minimally.

CONCLUSIONS: Causal-modeling techniques, each subject to different assumptions, demonstrated causal effects of locally generated pollutants on daily deaths with effects at concentrations below the current EPA daily [Formula: see text] standard. https://doi.org/10.1289/EHP2732.