Trends in air pollution and mortality: an approach to the assessment of unmeasured confounding

Long-term exposure to air pollution and COVID-19 incidence: A multi-country study

The study of the impacts of air pollution on COVID-19 has gained increasing attention. However, most of the existing studies are based on a single country, with a high degree of variation in the results reported in different papers. We attempt to inform the debate about the long-term effects of air pollution on COVID-19 by conducting a multi-country analysis using a spatial ecological design, including Canada, Italy, England and the United States. The model allows the residual spatial autocorrelation after accounting for covariates. It is concluded that the effects of PM_2.5 and NO2 are inconsistent across countries. Specifically, NO2 was not found to be an important factor affecting COVID-19 infection, while a large effect for PM_2.5 in the US is not found in the other three countries. The Population Attributable Fraction for COVID-19 incidence ranges from 3.4% in Canada to 45.9% in Italy, although with considerable uncertainty in these estimates.

A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines but have only recently begun to take hold in spatial applications. Spatial causal inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided.

Acute and Cumulative Effects of Haze Fine Particles on Mortality and the Seasonal Characteristics in Beijing, China, 2005-2013: A Time-Stratified Case-Crossover Study

We observed significant effects of particulate matter (PM_2.5) on cause-specific mortality by applying a time-stratified case-crossover and lag-structure designs in Beijing over a nine-year study period (2005–2013). The year-round odds ratio (OR) was 1.005 on the current day with a 10 μg/m³ increase in PM_2.5 for all-cause mortality. For cardiovascular mortality and stroke, the ORs were 1.007 and 1.008 on the current day, respectively. Meanwhile, during a lag of six days, the cumulative effects of haze on relative risk of mortality, respiratory mortality and all-cause mortality was in the range of 2~11%. Moreover, we found a significant seasonal pattern in the associations for respiratory mortality: significant associations were observed in spring and fall, while for all-cause mortality, cardiovascular mortality, cardiac and stroke, significant associations were observed in winter. Moreover, increasing temperature would decrease risks of mortalities in winter taking fall as the reference season. We concluded that in summer, temperature acted as a direct enhancer of air pollutants; while in winter and spring, it was an index of the diameter distribution and composition of fine particles.

Causal Modeling in Environmental Health

The field of environmental health has been dominated by modeling associations, especially by regressing an observed outcome on a linear or nonlinear function of observed covariates. Readers interested in advances in policies for improving environmental health are, however, expecting to be informed about health effects resulting from, or more explicitly caused by, environmental exposures. The quantification of health impacts resulting from the removal of environmental exposures involves causal statements. Therefore, when possible, causal inference frameworks should be considered for analyzing the effects of environmental exposures on health outcomes.

Impact of long-term temporal trends in fine particulate matter (PM2.5) on associations of annual PM2.5 exposure and mortality

Decreasing ambient fine particulate matter (PM_2.5) concentrations over time together with increasing life expectancy raise concerns about temporal confounding of associations between PM_2.5 and mortality. To address this issue, we examined PM_2.5-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 PM_2.5-associated MRRs vary by study period length. We then evaluated three approaches to control for temporal confounding: (1) assessing exposures using the residual of PM_2.5 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 PM_2.5 into the health model, with this term estimated using decomposition approaches. We found a 10 μg/m³ increase in PM_2.5 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 PM_2.5. 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 PM_2.5 can be confounded by long-term time trends, and this confounding can be controlled using the residuals of PM_2.5 regressed on time.

PM2.5 and ozone, indicators of air quality, and acute deaths in California, 2004-2007

Since the London Great Smog of 1952 was estimated to have killed over 4000 people, scientists have studied the relationship between air quality and acute mortality. Currently, the association between air quality and acute deaths is usually taken as evidence for causality. As air quality has markedly improved since 1952, do contemporary datasets support this view? We use a large dataset, eight air basins in California for the years 2004-2007, to examine the possible association of ozone and PM_2.5 with acute deaths after statistically removing seasonal and weather effects. Our analysis dataset is available on request. We conducted a regression-corrected, case-crossover analysis for all non-accidental deaths age 75 and older. We used stepwise regression to examine three causes of death. After seasonal and weather adjustments, there was essentially no predictive power of ozone or PM_2.5 for acute deaths. The case-crossover analysis produced odds ratio very close to 1.000 (no effect). The very narrow confidence limits indicated good statistical power. We study recent air quality in both time-stratified, symmetric, bidirectional case-crossover and time series regression and both give consistent results. There is no statistically significant association between either ozone or PM_2.5 and acute human mortality. In the absence of an association, causality is in question.

Local Control Strategy: Simple Analyses of Air Pollution Data Can Reveal Heterogeneity in Longevity Outcomes

Claims from observational studies that use traditional model specification searches often fail to replicate, partially because the available data tend to be biased. There is an urgent need for an alternative statistical analysis strategy, that is not only simple and easily understood but also is more likely to give reliable insights when the available data have not been designed and balanced. The alternative strategy known as local control first generates local, nonparametric effect-size estimates (fair treatment comparisons) and only then asks whether the observed variation in these local estimates can be predicted from potential confounding factors. Here, we illustrate application of local control to a historical air pollution data set describing a "natural experiment" initiated by the federal Clean Air Act Amendments of 1970. Our reanalysis reveals subgroup heterogeneity in the effects of air quality regulation on elderly longevity (one size does not fit all), and we show that this heterogeneity is largely explained by socioeconomic and environmental confounders other than air quality.

Air quality and acute deaths in California, 2000-2012

Many studies have shown an association between air quality and acute deaths, and such associations are widely interpreted as causal. Several factors call causation and even association into question, for example multiple testing and multiple modeling, publication bias and confirmation bias. Many published studies are difficult or impossible to reproduce because of lack of access to confidential data sources. Here we make publically available a dataset containing daily air quality levels, PM_2.5 and ozone, daily temperature levels, minimum and maximum and daily maximum relative humidity levels for the eight most populous California air basins, thirteen years, >2M deaths, over 37,000 exposure days. The data are analyzed using standard time series analysis, and a sensitivity analysis is computed varying model parameters, locations and years. Our analysis finds little evidence for association between air quality and acute deaths. These results are consistent with those for the widely cited NMMAPS dataset when the latter are restricted to California. The daily death variability was mostly explained by time of year or weather variables; Neither PM_2.5 nor ozone added appreciably to the prediction of daily deaths. These results call into question the widespread belief that association between air quality and acute deaths is causal/near-universal.

Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England

BACKGROUND

Estimating the long-term health impact of air pollution in a spatio-temporal ecological study requires representative concentrations of air pollutants to be constructed for each geographical unit and time period. Averaging concentrations in space and time is commonly carried out, but little is known about how robust the estimated health effects are to different aggregation functions. A second under researched question is what impact air pollution is likely to have in the future.

METHODS

We conducted a study for England between 2007 and 2011, investigating the relationship between respiratory hospital admissions and different pollutants: nitrogen dioxide (NO2); ozone (O3); particulate matter, the latter including particles with an aerodynamic diameter less than 2.5 micrometers (PM2.5), and less than 10 micrometers (PM10); and sulphur dioxide (SO2). Bayesian Poisson regression models accounting for localised spatio-temporal autocorrelation were used to estimate the relative risks (RRs) of pollution on disease risk, and for each pollutant four representative concentrations were constructed using combinations of spatial and temporal averages and maximums. The estimated RRs were then used to make projections of the numbers of likely respiratory hospital admissions in the 2050s attributable to air pollution, based on emission projections from a number of Representative Concentration Pathways (RCP).

RESULTS

NO2 exhibited the largest association with respiratory hospital admissions out of the pollutants considered, with estimated increased risks of between 0.9 and 1.6% for a one standard deviation increase in concentrations. In the future the projected numbers of respiratory hospital admissions attributable to NO2 in the 2050s are lower than present day rates under 3 Representative Concentration Pathways (RCPs): 2.6, 6.0, and 8.5, which is due to projected reductions in future NO2 emissions and concentrations.

CONCLUSIONS

NO2 concentrations exhibit consistent substantial present-day health effects regardless of how a representative concentration is constructed in space and time. Thus as concentrations are predicted to remain above limits set by European Union Legislation until the 2030s in parts of urban England, it will remain a substantial health risk for some time.

Controlling for unmeasured confounding and spatial misalignment in long-term air pollution and health studies

The health impact of long-term exposure to air pollution is now routinely estimated using spatial ecological studies, owing to the recent widespread availability of spatial referenced pollution and disease data. However, this areal unit study design presents a number of statistical challenges, which if ignored have the potential to bias the estimated pollution-health relationship. One such challenge is how to control for the spatial autocorrelation present in the data after accounting for the known covariates, which is caused by unmeasured confounding. A second challenge is how to adjust the functional form of the model to account for the spatial misalignment between the pollution and disease data, which causes within-area variation in the pollution data. These challenges have largely been ignored in existing long-term spatial air pollution and health studies, so here we propose a novel Bayesian hierarchical model that addresses both challenges and provide software to allow others to apply our model to their own data. The effectiveness of the proposed model is compared by simulation against a number of state-of-the-art alternatives proposed in the literature and is then used to estimate the impact of nitrogen dioxide and particulate matter concentrations on respiratory hospital admissions in a new epidemiological study in England in 2010 at the local authority level. © 2015 The Authors. Environmetrics published by John Wiley & Sons Ltd.

Enhancing the Characterization of Epistemic Uncertainties in PM2.5 Risk Analyses

The Environmental Benefits Mapping and Analysis Program (BenMAP) is a software tool developed by the U.S. Environmental Protection Agency (EPA) that is widely used inside and outside of EPA to produce quantitative estimates of public health risks from fine particulate matter (PM2.5 ). This article discusses the purpose and appropriate role of a risk analysis tool to support risk management deliberations, and evaluates the functions of BenMAP in this context. It highlights the importance in quantitative risk analyses of characterization of epistemic uncertainty, or outright lack of knowledge, about the true risk relationships being quantified. This article describes and quantitatively illustrates sensitivities of PM2.5 risk estimates to several key forms of epistemic uncertainty that pervade those calculations: the risk coefficient, shape of the risk function, and the relative toxicity of individual PM2.5 constituents. It also summarizes findings from a review of U.S.-based epidemiological evidence regarding the PM2.5 risk coefficient for mortality from long-term exposure. That review shows that the set of risk coefficients embedded in BenMAP substantially understates the range in the literature. We conclude that BenMAP would more usefully fulfill its role as a risk analysis support tool if its functions were extended to better enable and prompt its users to characterize the epistemic uncertainties in their risk calculations. This requires expanded automatic sensitivity analysis functions and more recognition of the full range of uncertainty in risk coefficients.

Preliminary evaluation of a regional atmospheric chemical data assimilation system for environmental surveillance

We report the progress of an ongoing effort by the Air Resources Laboratory, NOAA to build a prototype regional Chemical Analysis System (ARLCAS). The ARLCAS focuses on providing long-term analysis of the three dimensional (3D) air-pollutant concentration fields over the continental U.S. It leverages expertise from the NASA Earth Science Division-sponsored Air Quality Applied Science Team (AQAST) for the state-of-science knowledge in atmospheric and data assimilation sciences. The ARLCAS complies with national operational center requirement protocols and aims to have the modeling system to be maintained by a national center. Meteorology and chemistry observations consist of land-, air- and space-based observed and quality-assured data. We develop modularized testing to investigate the efficacies of the various components of the ARLCAS. The sensitivity testing of data assimilation schemes showed that with the increment of additional observational data sets, the accuracy of the analysis chemical fields also increased incrementally in varying margins. The benefit is especially noted for additional data sets based on a different platform and/or a different retrieval algorithm. We also described a plan to apply the analysis chemical fields in environmental surveillance at the Centers for Disease Control and Prevention.

A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in Greater London

It has long been known that air pollution is harmful to human health, as many epidemiological studies have been conducted into its effects. Collectively, these studies have investigated both the acute and chronic effects of pollution, with the latter typically based on individual level cohort designs that can be expensive to implement. As a result of the increasing availability of small-area statistics, ecological spatio-temporal study designs are also being used, with which a key statistical problem is allowing for residual spatio-temporal autocorrelation that remains after the covariate effects have been removed. We present a new model for estimating the effects of air pollution on human health, which allows for residual spatio-temporal autocorrelation, and a study into the long-term effects of air pollution on human health in Greater London, England. The individual and joint effects of different pollutants are explored, via the use of single pollutant models and multiple pollutant indices.

Science and regulation. Particulate matter matters

Quasi-experimental evidence is needed on the relations between human health and airborne particulate matter.

Controlling for localised spatio-temporal autocorrelation in long-term air pollution and health studies

Estimating the long-term health impact of air pollution using an ecological spatio-temporal study design is a challenging task, due to the presence of residual spatio-temporal autocorrelation in the health counts after adjusting for the covariate effects. This autocorrelation is commonly modelled by a set of random effects represented by a Gaussian Markov random field (GMRF) prior distribution, as part of a hierarchical Bayesian model. However, GMRF models typically assume the random effects are globally smooth in space and time, and thus are likely to be collinear to any spatially and temporally smooth covariates such as air pollution. Such collinearity leads to poor estimation performance of the estimated fixed effects, and motivated by this epidemiological problem, this paper proposes new GMRF methodology to allow for localised spatio-temporal smoothing. This means random effects that are either geographically or temporally adjacent are allowed to be autocorrelated or conditionally independent, which allows more flexible autocorrelation structures to be represented. This increased flexibility results in improved fixed effects estimation compared with global smoothing models, which is evidenced by our simulation study. The methodology is then applied to the motivating study investigating the long-term effects of air pollution on respiratory ill health in Greater Glasgow, Scotland between 2007 and 2011.

Comparison of exposure estimation methods for air pollutants: ambient monitoring data and regional air quality simulation

Air quality modeling could potentially improve exposure estimates for use in epidemiological studies. We investigated this application of air quality modeling by estimating location-specific (point) and spatially-aggregated (county level) exposure concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM(2.5)) and ozone (O(3)) for the eastern U.S. in 2002 using the Community Multi-scale Air Quality (CMAQ) modeling system and a traditional approach using ambient monitors. The monitoring approach produced estimates for 370 and 454 counties for PM(2.5) and O(3), respectively. Modeled estimates included 1861 counties, covering 50% more population. The population uncovered by monitors differed from those near monitors (e.g., urbanicity, race, education, age, unemployment, income, modeled pollutant levels). CMAQ overestimated O(3) (annual normalized mean bias=4.30%), while modeled PM(2.5) had an annual normalized mean bias of -2.09%, although bias varied seasonally, from 32% in November to -27% in July. Epidemiology may benefit from air quality modeling, with improved spatial and temporal resolution and the ability to study populations far from monitors that may differ from those near monitors. However, model performance varied by measure of performance, season, and location. Thus, the appropriateness of using such modeled exposures in health studies depends on the pollutant and metric of concern, acceptable level of uncertainty, population of interest, study design, and other factors.

Differential exposure to hazardous air pollution in the United States: a multilevel analysis of urbanization and neighborhood socioeconomic deprivation

Population exposure to multiple chemicals in air presents significant challenges for environmental public health. Air quality regulations distinguish criteria air pollutants (CAPs) (e.g., ozone, PM2.5) from hazardous air pollutants (HAPs)—187 chemicals which include carcinogens and others that are associated with respiratory, cardiovascular, neurological and numerous other non-cancer health effects. Evidence of the public’s cumulative exposure and the health effects of HAPs are quite limited. A multilevel model is used to assess differential exposure to HAP respiratory, neurological, and cancer hazards (2005) related to the Townsend Index of Socioeconomic Deprivation (TSI), after adjustment for regional population size and economic activity, and local population density. We found significant positive associations between tract TSI and respiratory and cancer HAP exposure hazards, and smaller effects for neurological HAPs. Tracts in the top quintile of TSI have between 38%–60% higher HAP exposure than the bottom quintile; increasing population size from the bottom quintile to the top quintile modifies HAP exposure hazard related to TSI, increasing cancer HAP exposure hazard by 6% to 20% and increasing respiratory HAP exposure hazard by 12% to 27%. This study demonstrates the value of social epidemiological methods for analyzing differential exposure and advancing cumulative risk assessment.

Bayesian effect estimation accounting for adjustment uncertainty

Model-based estimation of the effect of an exposure on an outcome is generally sensitive to the choice of which confounding factors are included in the model. We propose a new approach, which we call Bayesian adjustment for confounding (BAC), to estimate the effect of an exposure of interest on the outcome, while accounting for the uncertainty in the choice of confounders. Our approach is based on specifying two models: (1) the outcome as a function of the exposure and the potential confounders (the outcome model); and (2) the exposure as a function of the potential confounders (the exposure model). We consider Bayesian variable selection on both models and link the two by introducing a dependence parameter, ω, denoting the prior odds of including a predictor in the outcome model, given that the same predictor is in the exposure model. In the absence of dependence (ω= 1), BAC reduces to traditional Bayesian model averaging (BMA). In simulation studies, we show that BAC, with ω > 1, estimates the exposure effect with smaller bias than traditional BMA, and improved coverage. We, then, compare BAC, a recent approach of Crainiceanu, Dominici, and Parmigiani (2008, Biometrika 95, 635-651), and traditional BMA in a time series data set of hospital admissions, air pollution levels, and weather variables in Nassau, NY for the period 1999-2005. Using each approach, we estimate the short-term effects of on emergency admissions for cardiovascular diseases, accounting for confounding. This application illustrates the potentially significant pitfalls of misusing variable selection methods in the context of adjustment uncertainty.

An Approach to the Estimation of Chronic Air Pollution Effects Using Spatio-Temporal Information

There is substantial observational evidence that long-term exposure to particulate air pollution is associated with premature death in urban populations. Estimates of the magnitude of these effects derive largely from cross-sectional comparisons of adjusted mortality rates among cities with varying pollution levels. Such estimates are potentially confounded by other differences among the populations correlated with air pollution, for example, socioeconomic factors. An alternative approach is to study covariation of particulate matter and mortality across time within a city, as has been done in investigations of short-term exposures. In either event, observational studies like these are subject to confounding by unmeasured variables. Therefore the ability to detect such confounding and to derive estimates less affected by confounding are a high priority. In this article, we describe and apply a method of decomposing the exposure variable into components with variation at distinct temporal, spatial, and time by space scales, here focusing on the components involving time. Starting from a proportional hazard model, we derive a Poisson regression model and estimate two regression coefficients: the "global" coefficient that measures the association between national trends in pollution and mortality; and the "local" coefficient, derived from space by time variation, that measures the association between location-specific trends in pollution and mortality adjusted by the national trends. Absent unmeasured confounders and given valid model assumptions, the scale-specific coefficients should be similar; substantial differences in these coefficients constitute a basis for questioning the model. We derive a backfitting algorithm to fit our model to very large spatio-temporal datasets. We apply our methods to the Medicare Cohort Air Pollution Study (MCAPS), which includes individual-level information on time of death and age on a population of 18.2 million for the period 2000-2006. Results based on the global coefficient indicate a large increase in the national life expectancy for reductions in the yearly national average of PM2.5. However, this coefficient based on national trends in PM2.5 and mortality is likely to be confounded by other variables trending on the national level. Confounding of the local coefficient by unmeasured factors is less likely, although it cannot be ruled out. Based on the local coefficient alone, we are not able to demonstrate any change in life expectancy for a reduction in PM2.5. We use additional survey data available for a subset of the data to investigate sensitivity of results to the inclusion of additional covariates, but both coefficients remain largely unchanged.

Invited commentary: Epidemiologic studies of the health associations of environmental exposures with preterm birth

In this issue of the Journal, two different articles present epidemiologic evidence supporting the hypotheses that environmental exposures to particulate air pollution or higher temperatures modestly increase the risk of preterm birth. In this commentary, the author discusses environmental epidemiologic methods through the lens of these two papers with respect to the causal question, measurements, and quantification and interpretation of the evidence. Both groups of investigators present results from exploratory analyses that are at the hypothesis-generating end of the research spectrum as opposed to the confirmatory end. The present author describes in qualitative terms a method for decomposing evidence about the association of environmental exposures with prematurity into components representing different temporal and spatial scales. Finally, reproducible epidemiologic research methodology for studies like these is offered as one way to speed the transition from exploratory studies to confirmatory studies.

Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association

In 2004, the first American Heart Association scientific statement on "Air Pollution and Cardiovascular Disease" concluded that exposure to particulate matter (PM) air pollution contributes to cardiovascular morbidity and mortality. In the interim, numerous studies have expanded our understanding of this association and further elucidated the physiological and molecular mechanisms involved. The main objective of this updated American Heart Association scientific statement is to provide a comprehensive review of the new evidence linking PM exposure with cardiovascular disease, with a specific focus on highlighting the clinical implications for researchers and healthcare providers. The writing group also sought to provide expert consensus opinions on many aspects of the current state of science and updated suggestions for areas of future research. On the basis of the findings of this review, several new conclusions were reached, including the following: Exposure to PM <2.5 microm in diameter (PM(2.5)) over a few hours to weeks can trigger cardiovascular disease-related mortality and nonfatal events; longer-term exposure (eg, a few years) increases the risk for cardiovascular mortality to an even greater extent than exposures over a few days and reduces life expectancy within more highly exposed segments of the population by several months to a few years; reductions in PM levels are associated with decreases in cardiovascular mortality within a time frame as short as a few years; and many credible pathological mechanisms have been elucidated that lend biological plausibility to these findings. It is the opinion of the writing group that the overall evidence is consistent with a causal relationship between PM(2.5) exposure and cardiovascular morbidity and mortality. This body of evidence has grown and been strengthened substantially since the first American Heart Association scientific statement was published. Finally, PM(2.5) exposure is deemed a modifiable factor that contributes to cardiovascular morbidity and mortality.

The importance of scale for spatial-confounding bias and precision of spatial regression estimators

Residuals in regression models are often spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When unmeasured confounding introduces spatial structure into the residuals, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual: one can reduce bias by fitting a spatial model only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals are independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.

Critical considerations in evaluating scientific evidence of health effects of ambient ozone: a conference report

The U.S. Environmental Protection Agency (EPA), under the authority of the Clean Air Act (CAA), is required to promulgate National Ambient Air Quality Standards (NAAQSs) for criteria air pollutants, including ozone. Each NAAQS includes a primary health-based standard and a secondary or welfare-based standard. This paper considers only the science used for revision of the primary standard for ozone in 2008. This paper summarizes deliberations of a small group of scientists who met in June 2007 to review the scientific information informing the EPA Administrator's proposed revision of the 1997 standard. The Panel recognized that there is no scientific methodology that, in the absence of judgment, can define the precise numerical level, related averaging time, and statistical form of the NAAQS. The selection of these elements of the NAAQS involves policy judgments that should be informed by scientific information and analyses. Thus, the Panel members did not feel it appropriate to offer either their individual or collective judgment on the specific numerical level of the NAAQS for ozone. The Panel deliberations focused on the scientific data available on the health effects of exposure to ambient concentrations of ozone, controlled ozone exposure studies with human volunteers, long-term epidemiological studies, time- series epidemiological studies, human panel studies, and toxicological investigations. The deliberations also dealt with the issue of background levels of ozone of nonanthropogenic origin and issues involved with conducting formal risk assessments of the health impacts of current and prospective levels of ambient ozone. The scientific issues that were central to the EPA Administrator's 2008 revision of the NAAQS for ozone will undoubtedly also be critical to the next review of the ozone standard. That review should begin very soon if it is to be completed within the 5-year cycle specified in the CAA. It is hoped that this Report will stimulate discussion of these scientific issues, conduct of additional research, and conduct of new analyses that will provide an improved scientific basis for the policy judgment that will have to be made by a future EPA Administrator in considering potential revision of the ozone standard.

An approach for estimating the health effects of changes over time in air pollution: an illustration using cardio-respiratory hospital admissions in London

Objectives

First, we present a general analytical approach to estimating the association between medium-term changes in air pollution and health across small areas. As a specific illustration, we then applied the approach to data on London residents from a 4-year period to test whether reductions in traffic-related air pollution were associated with reductions in cardio-respiratory hospital admissions.

Methods

A binomial distribution was used to model change in admissions over time in each small area, which was measured as the proportion of admissions in 2003–2004 out of admissions over all study years (2001–2004). Annual average concentrations of nitrogen oxides (NO_x) were modelled using an emissions-dispersion model. The association between change in NO_x and change in hospital admissions was estimated using logistic regression and an instrumental variable approach.

Results

For some diagnostic groups, suggestive associations between reductions in NO_x and reductions in admissions were observed, for example, OR=0.97 (95% CI 0.96 to 0.99) for an IQR decrease in NO_x (3 μg/m³) and all respiratory admissions. Accounting for spatial dependence attenuated several of the associations; for respiratory admissions, the OR was 1.00 (95% CI 0.98 to 1.02), leaving only that for bronchiolitis significant (OR=0.91; 95% CI 0.84 to 0.99). In this particular illustration, the instrumental variable approach did not appear to add information.

Conclusions

In this illustration, there was relatively limited power to detect an association between changes in air pollution and hospital admissions over time. However, the analytical approach could deliver more robust estimates of the health effects of changes in air pollution in settings with greater spatial contrast in changes in air pollution over time.

Cumulative effects and threshold levels in air pollution mortality: data analysis of nine large US cities using the NMMAPS dataset

We examined the existence of thresholds, cumulative effects and the homogeneity of five air pollutants on the relative risk of three mortality outcomes using data from nine major US cities using data from NMMAPS. Overall, PM(10) (usually 200-day accumulation) and ozone (3-day accumulation) were the two important predictors of outcome but their effect was not uniform across the nine cities. Many models exhibited thresholds (25-45 microm g/m(3) for PM(10), and 10-45 ppb for O(3)). Our preliminary exploratory analyses suggest that the use of a linear, no threshold, model for pollution studies is not consistent with the observed data. The heterogeneity in the risk estimates across the nine cities suggests combining the local risk estimates to obtain a national risk estimate may not be justifiable and the estimate is likely to be confounded.

Short-term weather variability in Chicago and hospitalizations for Kawasaki disease

BACKGROUND

: Kawasaki disease exhibits a distinct seasonality, and short-term changes in weather may affect its occurrence.

METHODS

: To investigate the effects of weather variability on the occurrence of this syndrome, we conducted a time-between-events analysis of consecutive admissions for Kawasaki disease to a large pediatric hospital in Chicago. We used gamma regression to model the times between admissions. This is a novel application of gamma regression to model the time between admissions as a function of subject-specific covariates.

RESULTS

: We recorded 723 admissions in the 18-year (1986-2003) study period, of which 700 had complete data for analysis. Admissions for Kawasaki disease in Chicago were seasonal: The mean time between admissions was 34% shorter (relative time = 0.66, 95% confidence interval 0.54-0.81) from January-March than from July-September. In 1998, we recorded a larger number of admissions for Kawasaki disease (n = 65) than in other years (mean n = 37). January-March months of 1998 were warmer by a mean of 3 degrees C (1.5 degrees C-4.4 degrees C) and the mean time between admissions was 48% shorter (relative time = 0.52, 0.36-0.75) than in equivalent periods of other study years.

CONCLUSIONS

: Our findings show that atypical changes in weather affect the occurrence of Kawasaki disease and are compatible with a link to an infectious trigger. The analysis of interevent times using gamma regression is an alternative to Poisson regression in modeling a time series of sparse daily counts.

Can fine particulate matter explain the paradoxical ozone associations?

Our previous paper entitled "Paradoxical ozone associations could be due to methyl nitrite from combustion of methyl ethers or esters in engine fuels" (Env. Int.. 2007;33;1090) reviewed 11 studies of the impact of ozone on human health that, paradoxically, found a negative coefficient for ozone-morbidity associations. We argued that the most likely explanation for this effect would be methyl nitrite (MN) as an unsuspected exhaust component of engines with methyl ether in the fuel. The basis of the argument was the fact that MN is rapidly destroyed by sunlight, so that MN would be negatively correlated with ozone. All (but one) of the reviewed studies concluded that criterion pollutants could not explain the negative slope. The argument was strengthened by the observation that such paradoxical ozone associations have not been found in regions without significant methyl ether in gasoline. Left unaddressed in the previous paper was the possibility that fine particulate matter (FPM) might explain the POA. If this were true, then it would be necessary that the FPM be negatively correlated with ozone in those regions that found a POA. The current paper reviews data on FPM-ozone correlations in those regions where a POA was identified. The results show that FPM was, in most cases, positively correlated with ozone and so could not explain the POA.

Meeting report: atmospheric pollution and human reproduction

BACKGROUND

There is a growing body of epidemiologic literature reporting associations between atmospheric pollutants and reproductive outcomes, particularly birth weight and gestational duration.

OBJECTIVES

The objectives of our international workshop were to discuss the current evidence, to identify the strengths and weaknesses of published epidemiologic studies, and to suggest future directions for research.

DISCUSSION

Participants identified promising exposure assessment tools, including exposure models with fine spatial and temporal resolution that take into account time-activity patterns. More knowledge on factors correlated with exposure to air pollution, such as other environmental pollutants with similar temporal variations, and assessment of nutritional factors possibly influencing birth outcomes would help evaluate importance of residual confounding. Participants proposed a list of points to report in future publications on this topic to facilitate research syntheses. Nested case-control studies analyzed using two-phase statistical techniques and development of cohorts with extensive information on pregnancy behaviors and biological samples are promising study designs. Issues related to the identification of critical exposure windows and potential biological mechanisms through which air pollutants may lead to intrauterine growth restriction and premature birth were reviewed.

CONCLUSIONS

To make progress, this research field needs input from toxicology, exposure assessment, and clinical research, especially to aid in the identification and exposure assessment of feto-toxic agents in ambient air, in the development of early markers of adverse reproductive outcomes, and of relevant biological pathways. In particular, additional research using animal models would help better delineate the biological mechanisms underpinning the associations reported in human studies.

Confounding in air pollution epidemiology: the broader context

Recent research appropriately suggests that caution is warranted when interpreting long-term trends of improving air quality and trends in reduced mortality rates as causal because of concerns about confounding. The use of long-term time trends as the primary source of exposure variability, however, has been avoided in most air pollution epidemiology studies. Multiple studies, using different study designs that exploit various temporal and spatial dimensions of exposure variability, have observed reasonably consistent and coherent associations between particulate air pollution and cardiopulmonary health. It's possible that these air pollution studies have experienced "epidemiological bad luck" with multiple confounders that are inadequately controlled for and coincidentally cause spurious correlations that are coherent across the different study designs. To be consistent with the overall epidemiologic results, these confounders must correlate with pollution across various dimensions of time and space and be stronger risk factors for cardiopulmonary disease than for other disease. The more likely explanation of recent air pollution epidemiology is that air pollution, especially fine particulate and related pollution, has measurable effects on cardiopulmonary health.