Source-Apportioned PM2.5 and Cardiorespiratory Emergency Department Visits: Accounting for Source Contribution Uncertainty.

BACKGROUND: Despite evidence suggesting that air pollution-related health effects differ by emissions source, epidemiologic studies on fine particulate matter (PM2.5) infrequently differentiate between particles from different sources. Those that do rarely account for the uncertainty of source apportionment methods. METHODS: For each day in a 12-year period (1998-2010) in Atlanta, GA, we estimated daily PM2.5 source contributions from a Bayesian ensemble model that combined four source apportionment methods including chemical transport and receptor-based models. We fit Poisson generalized linear models to estimate associations between source-specific PM2.5 concentrations and cardiorespiratory emergency department visits (n = 1,598,117). We propagated uncertainty in the source contribution estimates through analyses using multiple imputation. RESULTS: Respiratory emergency department visits were positively associated with biomass burning and secondary organic carbon. For a 1 µg/m increase in PM2.5 from biomass burning during the past 3 days, the rate of visits for all respiratory outcomes increased by 0.4% (95% CI 0.0%, 0.7%). There was less evidence for associations between PM2.5 sources and cardiovascular outcomes, with the exception of ischemic stroke, which was positively associated with most PM2.5 sources. Accounting for the uncertainty of source apportionment estimates resulted, on average, in an 18% increase in the standard error for rate ratio estimates for all respiratory and cardiovascular emergency department visits, but inflation varied across specific sources and outcomes, ranging from 2% to 39%. CONCLUSIONS: This study provides evidence of associations between PM2.5 sources and some cardiorespiratory outcomes and quantifies the impact of accounting for variability in source apportionment approaches.

Review of Acellular Assays of Ambient Particulate Matter Oxidative Potential: Methods and Relationships with Composition, Sources, and Health Effects.

Oxidative stress is a potential mechanism of action for particulate matter (PM) toxicity and can occur when the body's antioxidant capacity cannot counteract or detoxify harmful effects of reactive oxygen species (ROS) due to an excess presence of ROS. ROS are introduced to the body via inhalation of PM with these species present on and/or within the particles (particle-bound ROS) and/or through catalytic generation of ROS in vivo after inhaling redox-active PM species (oxidative potential, OP). The recent development of acellular OP measurement techniques has led to a surge in research across the globe. In this review, particle-bound ROS techniques are discussed briefly while OP measurements are the focus due to an increasing number of epidemiologic studies using OP measurements showing associations with adverse health effects in some studies. The most common OP measurement techniques, including the dithiothreitol assay, glutathione assay, and ascorbic acid assay, are discussed along with evidence for utility of OP measurements in epidemiologic studies and PM characteristics that drive different responses between assay types (such as species composition, emission source, and photochemistry). Overall, most OP assays respond to metals like copper than can be found in emission sources like vehicles. Some OP assays respond to organics, especially photochemically aged organics, from sources like biomass burning. Select OP measurements have significant associations with certain cardiorespiratory end points, such as asthma, congestive heart disease, and lung cancer. In fact, multiple studies have found that exposure to OP measured using the dithiothreitol and glutathione assays drives higher risk ratios for certain cardiorespiratory outcomes than PM mass, suggesting OP measurements may be integrating the health-relevant fraction of PM and will be useful tools for future health analyses. The compositional impacts, including species and emission sources, on OP could have serious implications for health-relevant PM exposure. Though more work is needed, OP assays show promise for health studies as they integrate the impacts of PM species and properties on catalytic redox reactions into one measurement, and current work highlights the importance of metals, organic carbon, vehicles, and biomass burning emissions to PM exposures that could impact health.

Exposure to Mobile Source Air Pollution in Early-life and Childhood Asthma Incidence: The Kaiser Air Pollution and Pediatric Asthma Study.

BACKGROUND: Early-life exposure to traffic-related air pollution exacerbates childhood asthma, but it is unclear what role it plays in asthma development. METHODS: The association between exposure to primary mobile source pollutants during pregnancy and during infancy and asthma incidence by ages 2 through 6 was examined in the Kaiser Air Pollution and Pediatric Asthma Study, a racially diverse birth cohort of 24,608 children born between 2000 and 2010 and insured by Kaiser Permanente Georgia. We estimated concentrations of mobile source fine particulate matter (PM2.5, µg/m), nitrogen oxides (NOX, ppb), and carbon monoxide (CO, ppm) at the maternal and child residence using a Research LINE source dispersion model for near-surface releases. Asthma was defined using diagnoses and medication dispensings from medical records. We used binomial generalized linear regression to model the impact of exposure continuously and by quintiles on asthma risk. RESULTS: Controlling for covariates and modeling log-transformed exposure, a 2.7-fold increase in first year of life PM2.5 was associated with an absolute 4.1% (95% confidence interval, 1.6%, 6.6%) increase in risk of asthma by age 5. Quintile analysis showed an increase in risk from the first to second quintile, but similar risk across quintiles 2-5. Risk differences increased with follow-up age. Results were similar for NOX and CO and for exposure during pregnancy and the first year of life owing to high correlation. CONCLUSIONS: Results provide limited evidence for an association of early-life mobile source air pollution with childhood asthma incidence with a steeper concentration-response relationship observed at lower levels of exposure.

Estimating Acute Cardiorespiratory Effects of Ambient Volatile Organic Compounds.

BACKGROUND: The health effects of ambient volatile organic compounds (VOCs) have received less attention in epidemiologic studies than other commonly measured ambient pollutants. In this study, we estimated acute cardiorespiratory effects of ambient VOCs in an urban population. METHODS: Daily concentrations of 89 VOCs were measured at a centrally-located ambient monitoring site in Atlanta and daily counts of emergency department visits for cardiovascular diseases and asthma in the five-county Atlanta area were obtained for the 1998-2008 period. To understand the health effects of the large number of species, we grouped these VOCs a priori by chemical structure and estimated the associations between VOC groups and daily counts of emergency department visits in a time-series framework using Poisson regression. We applied three analytic approaches to estimate the VOC group effects: an indicator pollutant approach, a joint effect analysis, and a random effect meta-analysis, each with different assumptions. We performed sensitivity analyses to evaluate copollutant confounding. RESULTS: Hydrocarbon groups, particularly alkenes and alkynes, were associated with emergency department visits for cardiovascular diseases, while the ketone group was associated with emergency department visits for asthma. CONCLUSIONS: The associations observed between emergency department visits for cardiovascular diseases and alkenes and alkynes may reflect the role of traffic exhaust, while the association between asthma visits and ketones may reflect the role of secondary organic compounds. The different patterns of associations we observed for cardiovascular diseases and asthma suggest different modes of action of these pollutants or the mixtures they represent.

Method for Fusing Observational Data and Chemical Transport Model Simulations To Estimate Spatiotemporally Resolved Ambient Air Pollution.

Investigations of ambient air pollution health effects rely on complete and accurate spatiotemporal air pollutant estimates. Three methods are developed for fusing ambient monitor measurements and 12 km resolution chemical transport model (CMAQ) simulations to estimate daily air pollutant concentrations across Georgia. Temporal variance is determined by observations in one method, with the annual mean CMAQ field providing spatial structure. A second method involves scaling daily CMAQ simulated fields using mean observations to reduce bias. Finally, a weighted average of these results based on prediction of temporal variance provides optimized daily estimates for each 12 × 12 km grid. These methods were applied to daily metrics of 12 pollutants (CO, NO2, NOx, O3, SO2, PM10, PM2.5, and five PM2.5 components) over the state of Georgia for a seven-year period (2002-2008). Cross-validation demonstrates a wide range in optimized model performance across pollutants, with SO2 predicted most poorly due to limitations in coal combustion plume monitoring and modeling. For the other pollutants studied, 54-88% of the spatiotemporal variance (Pearson R(2) from cross-validation) was captured, with ozone and PM2.5 predicted best. The optimized fusion approach developed provides daily spatial field estimates of air pollutant concentrations and uncertainties that are consistent with observations, emissions, and meteorology.

Pediatric emergency department visits and ambient Air pollution in the U.S. State of Georgia: a case-crossover study.

BACKGROUND: Estimating the health effects of ambient air pollutant mixtures is necessary to understand the risk of real-life air pollution exposures. METHODS: Pediatric Emergency Department (ED) visit records for asthma or wheeze (n = 148,256), bronchitis (n = 84,597), pneumonia (n = 90,063), otitis media (n = 422,268) and upper respiratory tract infection (URI) (n = 744,942) were obtained from Georgia hospitals during 2002-2008. Spatially-contiguous daily concentrations of 11 ambient air pollutants were estimated from CMAQ model simulations that were fused with ground-based measurements. Using a case-crossover study design, odds ratios for 3-day moving average air pollutant concentrations were estimated using conditional logistic regression, matching on ZIP code, day-of-week, month, and year. RESULTS: In multipollutant models, the association of highest magnitude observed for the asthma/wheeze outcome was with "oxidant gases" (O3, NO2, and SO2); the joint effect estimate for an IQR increase of this mixture was OR: 1.068 (95% CI: 1.040, 1.097). The group of "secondary pollutants" (O3 and the PM2.5 components SO42-, NO3-, and NH4+) was strongly associated with bronchitis (OR: 1.090, 95% CI: 1.050, 1.132), pneumonia (OR: 1.085, 95% CI: 1.047, 1.125), and otitis media (OR: 1.059, 95% CI: 1.042, 1.077). ED visits for URI were strongly associated with "oxidant gases," "secondary pollutants," and the "criteria pollutants" (O3, NO2, CO, SO2, and PM2.5). CONCLUSIONS: Short-term exposures to air pollution mixtures were associated with ED visits for several different pediatric respiratory diseases.

Reactive Oxygen Species Generation Linked to Sources of Atmospheric Particulate Matter and Cardiorespiratory Effects.

Exposure to atmospheric fine particulate matter (PM2.5) is associated with cardiorespiratory morbidity and mortality, but the mechanisms are not well understood. We assess the hypothesis that PM2.5 induces oxidative stress in the body via catalytic generation of reactive oxygen species (ROS). A dithiothreitol (DTT) assay was used to measure the ROS-generation potential of water-soluble PM2.5. Source apportionment on ambient (Atlanta, GA) PM2.5 was performed using the chemical mass balance method with ensemble-averaged source impact profiles. Linear regression analysis was used to relate PM2.5 emission sources to ROS-generation potential and to estimate historical levels of DTT activity for use in an epidemiologic analysis for the period of 1998-2009. Light-duty gasoline vehicles (LDGV) exhibited the highest intrinsic DTT activity, followed by biomass burning (BURN) and heavy-duty diesel vehicles (HDDV) (0.11 ± 0.02, 0.069 ± 0.02, and 0.052 ± 0.01 nmol min(-1) µg(-1)source, respectively). BURN contributed the largest fraction to total DTT activity over the study period, followed by LDGV and HDDV (45, 20, and 14%, respectively). DTT activity was more strongly associated with emergency department visits for asthma/wheezing and congestive heart failure than PM2.5. This work provides further epidemiologic evidence of a biologically plausible mechanism, that of oxidative stress, for associations of adverse health outcomes with PM2.5 mass and supports continued assessment of the utility of the DTT activity assay as a measure of ROS-generating potential of particles.

Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications.

BACKGROUND: Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks. OBJECTIVE: Present a methodology allowing health investigators to explore associations between categories of ambient air quality days (i.e., multipollutant day types) and adverse health. METHODS: First, we applied a self-organizing map (SOM) to daily air quality data for 10 pollutants collected between January 1999 and December 2008 at a central monitoring location in Atlanta, Georgia to define a collection of multipollutant day types. Next, we conducted an epidemiologic analysis using our categories as a multipollutant metric of ambient air quality and daily counts of emergency department (ED) visits for asthma or wheeze among children aged 5 to 17 as the health endpoint. We estimated rate ratios (RR) for the association of multipollutant day types and pediatric asthma ED visits using a Poisson generalized linear model controlling for long-term, seasonal, and weekday trends and weather. RESULTS: Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed). On days with only NO3 elevated (which tended to be relatively cool) and on days when only SO2 was elevated (which likely reflected plume touchdowns from coal combustion point sources), estimated associations were modestly positive but confidence intervals included the null. CONCLUSIONS: We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others. Relative to other health endpoints, asthma exacerbation may be driven more by total ambient pollutant exposure than by composition.

Associations between ambient air pollutant mixtures and pediatric asthma emergency department visits in three cities: a classification and regression tree approach.

BACKGROUND: Characterizing multipollutant health effects is challenging. We use classification and regression trees to identify multipollutant joint effects associated with pediatric asthma exacerbations and compare these results with those from a multipollutant regression model with continuous joint effects. METHODS: We investigate the joint effects of ozone, NO2 and PM2.5 on emergency department visits for pediatric asthma in Atlanta (1999-2009), Dallas (2006-2009) and St. Louis (2001-2007). Daily concentrations of each pollutant were categorized into four levels, resulting in 64 different combinations or "Day-Types" that can occur. Days when all pollutants were in the lowest level were withheld as the reference group. Separate regression trees were grown for each city, with partitioning based on Day-Type in a model with control for confounding. Day-Types that appeared together in the same terminal node in all three trees were considered to be mixtures of potential interest and were included as indicator variables in a three-city Poisson generalized linear model with confounding control and rate ratios calculated relative to the reference group. For comparison, we estimated analogous joint effects from a multipollutant Poisson model that included terms for each pollutant, with concentrations modeled continuously. RESULTS AND DISCUSSION: No single mixture emerged as the most harmful. Instead, the rate ratios for the mixtures suggest that all three pollutants drive the health association, and that the rate plateaus in the mixtures with the highest concentrations. In contrast, the results from the comparison model are dominated by an association with ozone and suggest that the rate increases with concentration. CONCLUSION: The use of classification and regression trees to identify joint effects may lead to different conclusions than multipollutant models with continuous joint effects and may serve as a complementary approach for understanding health effects of multipollutant mixtures.

Air pollution and acute respiratory infections among children 0-4 years of age: an 18-year time-series study.

Upper and lower respiratory infections are common in early childhood and may be exacerbated by air pollution. We investigated short-term changes in ambient air pollutant concentrations, including speciated particulate matter less than 2.5 µm in diameter (PM2.5), in relation to emergency department (ED) visits for respiratory infections in young children. Daily counts of ED visits for bronchitis and bronchiolitis (n = 80,399), pneumonia (n = 63,359), and upper respiratory infection (URI) (n = 359,246) among children 0-4 years of age were collected from hospitals in the Atlanta, Georgia, area for the period 1993-2010. Daily pollutant measurements were combined across monitoring stations using population weighting. In Poisson generalized linear models, 3-day moving average concentrations of ozone, nitrogen dioxide, and the organic carbon fraction of particulate matter less than 2.5 µm in diameter (PM2.5) were associated with ED visits for pneumonia and URI. Ozone associations were strongest and were observed at low (cold-season) concentrations; a 1-interquartile range increase predicted a 4% increase (95% confidence interval: 2%, 6%) in visits for URI and an 8% increase (95% confidence interval: 4%, 13%) in visits for pneumonia. Rate ratios tended to be higher in the 1- to 4-year age group compared with infants. Results suggest that primary traffic pollutants, ozone, and the organic carbon fraction of PM2.5 exacerbate upper and lower respiratory infections in early life, and that the carbon fraction of PM2.5 is a particularly harmful component of the ambient particulate matter mixture.

Modification of the effect of ambient air pollution on pediatric asthma emergency visits: susceptible subpopulations.

BACKGROUND: Children may have differing susceptibility to ambient air pollution concentrations depending on various background characteristics of the children. METHODS: Using emergency department (ED) data linked with birth records from Atlanta, Georgia, we identified ED visits for asthma or wheeze among children 2 to 16 years of age from 1 January 2002 through 30 June 2010 (n = 109,758). We stratified by preterm delivery, term low birth weight, maternal race, Medicaid status, maternal education, maternal smoking, delivery method, and history of a bronchiolitis ED visit. Population-weighted daily average concentrations were calculated for 1-hour maximum carbon monoxide and nitrogen dioxide; 8-hour maximum ozone; and 24-hour average particulate matter less than 10 microns in diameter, particulate matter less than 2.5 microns in diameter (PM2.5), and the PM2.5 components sulfate, nitrate, ammonium, elemental carbon, and organic carbon, using measurements from stationary monitors. Poisson time-series models were used to estimate rate ratios for associations between 3-day moving average pollutant concentrations and daily ED visit counts and to investigate effect-measure modification by the stratification factors. RESULTS: Associations between pollutant concentrations and asthma exacerbations were larger among children born preterm and among children born to African American mothers. Stratification by race and preterm status together suggested that both factors affected susceptibility. The largest estimated effect size (for an interquartile range increase in pollution) was observed for ozone among preterm births to African American mothers: rate ratio = 1.138 (95% confidence interval = 1.077-1.203). In contrast, the rate ratio for the ozone association among full-term births to mothers of other races was 1.025 (0.970-1.083). CONCLUSIONS: Results support the hypothesis that children vary in their susceptibility to ambient air pollutants.

Using self-organizing maps to develop ambient air quality classifications: a time series example.

BACKGROUND: Development of exposure metrics that capture features of the multipollutant environment are needed to investigate health effects of pollutant mixtures. This is a complex problem that requires development of new methodologies. OBJECTIVE: Present a self-organizing map (SOM) framework for creating ambient air quality classifications that group days with similar multipollutant profiles. METHODS: Eight years of day-level data from Atlanta, GA, for ten ambient air pollutants collected at a central monitor location were classified using SOM into a set of day types based on their day-level multipollutant profiles. We present strategies for using SOM to develop a multipollutant metric of air quality and compare results with more traditional techniques. RESULTS: Our analysis found that 16 types of days reasonably describe the day-level multipollutant combinations that appear most frequently in our data. Multipollutant day types ranged from conditions when all pollutants measured low to days exhibiting relatively high concentrations for either primary or secondary pollutants or both. The temporal nature of class assignments indicated substantial heterogeneity in day type frequency distributions (~1%-14%), relatively short-term durations (<2 day persistence), and long-term and seasonal trends. Meteorological summaries revealed strong day type weather dependencies and pollutant concentration summaries provided interesting scenarios for further investigation. Comparison with traditional methods found SOM produced similar classifications with added insight regarding between-class relationships. CONCLUSION: We find SOM to be an attractive framework for developing ambient air quality classification because the approach eases interpretation of results by allowing users to visualize classifications on an organized map. The presented approach provides an appealing tool for developing multipollutant metrics of air quality that can be used to support multipollutant health studies.

Ambient pollen concentrations and emergency department visits for asthma and wheeze.

BACKGROUND: Previous studies report associations between aeroallergen exposure and asthma exacerbations. Aeroallergen burdens and asthma prevalence are increasing worldwide and are projected to increase further with climate change, highlighting the importance of understanding population-level relationships between ambient pollen concentrations and asthma. OBJECTIVE: We sought to examine short-term associations between ambient concentrations of various pollen taxa and emergency department (ED) visits for asthma and wheeze in the Atlanta metropolitan area between 1993 and 2004. METHODS: We assessed associations between the 3-day moving average (lag 0-1-2) of Betulaceae (except Alnus species), Cupressaceae, Quercus species, Pinaceae (except Tsuga species), Poaceae, and Ambrosia species pollen concentrations and daily asthma and wheeze ED visit counts, controlling for covarying pollen taxa and ambient pollutant concentrations. RESULTS: We observed a 2% to 3% increase in asthma- and wheeze-related ED visits per SD increase in Quercus species and Poaceae pollen and a 10% to 15% increased risk on days with the highest concentrations (comparing the top 5% of days with the lowest 50% of days). An SD increase in Cupressaceae concentrations was associated with a 1% decrease in ED visits. The association for Quercus species pollen was strongest for children aged 5 to 17 years. Effects of Ambrosia species pollen on asthma exacerbations were difficult to assess in this large-scale temporal analysis because of possible confounding by the steep increase in circulating rhinoviruses every September. CONCLUSION: Poaceae and Quercus species pollen contribute to asthma morbidity in Atlanta. Altered Quercus species and Poaceae pollen production caused by climate change could affect allergen-induced asthma morbidity in the southeastern United States.

Characterization of Ambient Air Pollution Measurement Error in a Time-Series Health Study using a Geostatistical Simulation Approach.

In recent years, geostatistical modeling has been used to inform air pollution health studies. In this study, distributions of daily ambient concentrations were modeled over space and time for 12 air pollutants. Simulated pollutant fields were produced for a 6-year time period over the 20-county metropolitan Atlanta area using the Stanford Geostatistical Modeling Software (SGeMS). These simulations incorporate the temporal and spatial autocorrelation structure of ambient pollutants, as well as season and day-of-week temporal and spatial trends; these fields were considered to be the true ambient pollutant fields for the purposes of the simulations that followed. Simulated monitor data at the locations of actual monitors were then generated that contain error representative of instrument imprecision. From the simulated monitor data, four exposure metrics were calculated: central monitor and unweighted, population-weighted, and area-weighted averages. For each metric, the amount and type of error relative to the simulated pollutant fields are characterized and the impact of error on an epidemiologic time-series analysis is predicted. The amount of error, as indicated by a lack of spatial autocorrelation, is greater for primary pollutants than for secondary pollutants and is only moderately reduced by averaging across monitors; more error will result in less statistical power in the epidemiologic analysis. The type of error, as indicated by the correlations of error with the monitor data and with the true ambient concentration, varies with exposure metric, with error in the central monitor metric more of the classical type (i.e., independent of the monitor data) and error in the spatial average metrics more of the Berkson type (i.e., independent of the true ambient concentration). Error type will affect the bias in the health risk estimate, with bias toward the null and away from the null predicted depending on the exposure metric; population-weighting yielded the least bias.

Development of outcome-based, multipollutant mobile source indicators.

Multipollutant indicators of mobile source impacts are developed from readily available CO, NOx, and elemental carbon (EC) data for use in air quality and epidemiologic analysis. Two types of outcome-based Integrated Mobile Source Indicators (IMSI) are assessed. The first is derived from analysis of emissions of EC, CO, and NOx such that pollutant concentrations are mixed and weighted based on emission ratios for both gasoline and diesel vehicles. The emission-based indicators (IMSI(EB)) capture the impact of mobile sources on air quality estimated from receptor models and their uncertainty is comparable to measurement and source apportionment uncertainties. The IMSI(EB) have larger correlation between two different receptor sites impacted by traffic than single pollutants, suggesting they are better indicators of the local impact ofmobile sources. A sensitivity analysis of fractions of pollutants in a two-pollutant mixture and the inclusion in an epidemiologic model is conducted to develop a second set of indicators based on health outcomes. The health-based indicators (IMSI(HB)) are weighted combinations of CO, NOx, and EC pairs that have the lowest P value in their association with cardiovascular disease emergency department visits, possibly due to their better spatial representativeness. These outcome-based, multipollutant indicators can provide support for the setting of multipollutant air quality standards and other air quality management activities.

A method for detection of residual confounding in time-series and other observational studies.

BACKGROUND: A difficult issue in observational studies is assessment of whether important confounders are omitted or misspecified. In this study, we present a method for assessing whether residual confounding is present. Our method depends on availability of an indicator with 2 key characteristics: first, it is conditionally independent (given measured exposures and covariates) of the outcome in the absence of confounding, misspecification, and measurement errors; second, it is associated with the exposure and, like the exposure, with any unmeasured confounders. METHODS: We demonstrate the method using a time-series study of the effects of ozone on emergency department visits for asthma in Atlanta. We argue that future air pollution may have the characteristics appropriate for an indicator, in part because future ozone cannot have caused yesterday's health events. Using directed acyclic graphs and specific causal relationships, we show that one can identify residual confounding using an indicator with the stated characteristics. We use simulations to assess the discriminatory ability of future ozone as an indicator of residual confounding in the association of ozone with asthma-related emergency department visits. Parameter choices are informed by observed data for ozone, meteorologic factors, and asthma. RESULTS: In simulations, we found that ozone concentrations 1 day after the emergency department visits had excellent discriminatory ability to detect residual confounding by some factors that were intentionally omitted from the model, but weaker ability for others. Although not the primary goal, the indicator can also signal other forms of modeling errors, including substantial measurement error, and does not distinguish between them. CONCLUSIONS: The simulations illustrate that the indicator based on future air pollution levels can have excellent discriminatory ability for residual confounding, although performance varied by situation. Application of the method should be evaluated by considering causal relationships for the intended application, and should be accompanied by other approaches, including evaluation of a priori knowledge.

A method to detect residual confounding in spatial and other observational studies.

BACKGROUND: Residual confounding is challenging to detect. Recently, we described a method for detecting confounding and justified it primarily for time-series studies. The method depends on an indicator with 2 key characteristics: (1) it is conditionally independent (given measured exposures and covariates) of the outcome, in the absence of confounding, misspecification, and measurement errors; and (2) like the exposure, it is associated with confounders, possibly unmeasured. We proposed using future exposure levels as the indicator to detect residual confounding. This choice seems natural for time-series studies because future exposure cannot have caused the event, yet they could be spuriously related to it. A related question addressed here is whether an analogous indicator can be used to identify residual confounding in a study based on spatial, rather than temporal, contrasts. METHODS: Using directed acyclic graphs, we show that future air pollution levels may have the characteristics appropriate for an indicator of residual confounding in spatial studies of environmental exposures. We empirically evaluate performance for spatial studies using simulations. RESULTS: In simulations based on a spatial study of ambient air pollution levels and birth weight in Atlanta, and using ambient air pollution 1 year after conception as the indicator, we were able to detect residual confounding. The discriminatory ability approached 100% for some factors intentionally omitted from the model, but was very weak for others. CONCLUSION: The simulations illustrate that an indicator based on future exposures can have excellent ability to detect residual confounding in spatial studies, although performance varied by situation.

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies.

BACKGROUND: Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta. METHODS: Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits. RESULTS: Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed. CONCLUSIONS: For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.

Implications of different approaches for characterizing ambient air pollutant concentrations within the urban airshed for time-series studies and health benefits analyses.

BACKGROUND: In time-series studies of the health effects of urban air pollutants, decisions must be made about how to characterize pollutant levels within the airshed. METHODS: Emergency department visits for pediatric asthma exacerbations were collected from Atlanta hospitals. Concentrations of carbon monoxide, nitrogen dioxide, ozone, sulfur dioxide, particulate matter less than 10 microns in diameter (PM10), particulate matter less than 2.5 microns in diameter (PM2.5), and the PM2.5 components elemental carbon, organic carbon, and sulfate were obtained from networks of ambient air quality monitors. For each pollutant we created three different daily metrics. For one metric we used the measurements from a centrally-located monitor; for the second we averaged measurements across the network of monitors; and for the third we estimated the population-weighted average concentration using an isotropic spatial model. Rate ratios for each of the metrics were estimated from time-series models. RESULTS: For pollutants with relatively homogeneous spatial distributions we observed only small differences in the rate ratio across the three metrics. Conversely, for spatially heterogeneous pollutants we observed larger differences in the rate ratios. For a given pollutant, the strength of evidence for an association (i.e., chi-square statistics) tended to be similar across metrics. CONCLUSIONS: Given that the chi-square statistics were similar across the metrics, the differences in the rate ratios for the spatially heterogeneous pollutants may seem like a relatively small issue. However, these differences are important for health benefits analyses, where results from epidemiological studies on the health effects of pollutants (per unit change in concentration) are used to predict the health impacts of a reduction in pollutant concentrations. We discuss the relative merits of the different metrics as they pertain to time-series studies and health benefits analyses.

Short-term associations between ambient air pollutants and pediatric asthma emergency department visits.

RATIONALE: Certain outdoor air pollutants cause asthma exacerbations in children. To advance understanding of these relationships, further characterization of the dose-response and pollutant lag effects are needed, as are investigations of pollutant species beyond the commonly measured criteria pollutants. OBJECTIVES: Investigate short-term associations between ambient air pollutant concentrations and emergency department visits for pediatric asthma. METHODS: Daily counts of emergency department visits for asthma or wheeze among children aged 5 to 17 years were collected from 41 Metropolitan Atlanta hospitals during 1993-2004 (n = 91,386 visits). Ambient concentrations of gaseous pollutants and speciated particulate matter were available from stationary monitors during this time period. Rate ratios for the warm season (May to October) and cold season (November to April) were estimated using Poisson generalized linear models in the framework of a case-crossover analysis. MEASUREMENTS AND MAIN RESULTS: Both ozone and primary pollutants from traffic sources were associated with emergency department visits for asthma or wheeze; evidence for independent effects of ozone and primary pollutants from traffic sources were observed in multipollutant models. These associations tended to be of the highest magnitude for concentrations on the day of the emergency department visit and were present at relatively low ambient concentrations. CONCLUSIONS: Even at relatively low ambient concentrations, ozone and primary pollutants from traffic sources independently contributed to the burden of emergency department visits for pediatric asthma.