Joint effects of ambient air pollutants on pediatric asthma emergency department visits in Atlanta, 1998-2004.
ABSTRACT: Because ambient air pollution exposure occurs as mixtures, consideration of joint effects of multiple pollutants may advance our understanding of the health effects of air pollution.We assessed the joint effect of air pollutants on pediatric asthma emergency department visits in Atlanta during 1998-2004. We selected combinations of pollutants that were representative of oxidant gases and secondary, traffic, power plant, and criteria pollutants, constructed using combinations of criteria pollutants and fine particulate matter (PM2.5) components. Joint effects were assessed using multipollutant Poisson generalized linear models controlling for time trends, meteorology, and daily nonasthma upper respiratory emergency department visit counts. Rate ratios (RRs) were calculated for the combined effect of an interquartile range increment in each pollutant's concentration.Increases in all of the selected pollutant combinations were associated with increases in warm-season pediatric asthma emergency department visits (eg, joint-effect RR = 1.13 [95% confidence interval = 1.06-1.21] for criteria pollutants, including ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, and PM2.5). Cold-season joint effects from models without nonlinear effects were generally weaker than warm-season effects. Joint-effect estimates from multipollutant models were often smaller than estimates based on single-pollutant models, due to control for confounding. Compared with models without interactions, joint-effect estimates from models including first-order pollutant interactions were largely similar. There was evidence of nonlinear cold-season effects.Our analyses illustrate how consideration of joint effects can add to our understanding of health effects of multipollutant exposures and also illustrate some of the complexities involved in calculating and interpreting joint effects of multiple pollutants.
Project description: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.
Project description:BACKGROUND: Although urban air pollution is a complex mix containing multiple constituents, studies of the health effects of long-term exposure often focus on a single pollutant as a proxy for the entire mixture. A better understanding of the component pollutant concentrations and interrelationships would be useful in epidemiological studies that exploit spatial differences in exposure by clarifying the extent to which measures of individual pollutants, particularly nitrogen dioxide (NO2), represent spatial patterns in the multipollutant mixture. OBJECTIVES: We examined air pollutant concentrations and interrelationships at the intraurban scale to obtain insight into the nature of the urban mixture of air pollutants. METHODS: Mobile measurements of 23 air pollutants were taken systematically at high resolution in Montreal, Quebec, Canada, over 34 days in the winter, summer, and autumn of 2009. RESULTS: We observed variability in pollution levels and in the statistical correlations between different pollutants according to season and neighborhood. Nitrogen oxide species (nitric oxide, NO2, nitrogen oxides, and total oxidized nitrogen species) had the highest overall spatial correlations with the suite of pollutants measured. Ultrafine particles and hydrocarbon-like organic aerosol concentration, a derived measure used as a specific indicator of traffic particles, also had very high correlations. CONCLUSIONS: Our findings indicate that the multipollutant mix varies considerably throughout the city, both in time and in space, and thus, no single pollutant would be a perfect proxy measure for the entire mix under all circumstances. However, based on overall average spatial correlations with the suite of pollutants measured, nitrogen oxide species appeared to be the best available indicators of spatial variation in exposure to the outdoor urban air pollutant mixture.
Project description: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.
Project description:BACKGROUND:The current single-pollutant approach to regulating ambient air pollutants is effective at protecting public health, but efficiencies may be gained by addressing issues in a multipollutant context since multiple pollutants often have common sources and individuals are exposed to more than one pollutant at a time. OBJECTIVE:We performed a cross-disciplinary review of the effects of multipollutant exposures on cardiovascular effects. METHODS:A broad literature search for references including at least two criteria air pollutants (particulate matter [PM], ozone [O3], oxides of nitrogen, sulfur oxides, carbon monoxide) was conducted. References were culled based on scientific discipline then searched for terms related to cardiovascular disease. Most multipollutant epidemiologic and experimental (i.e., controlled human exposure, animal toxicology) studies examined PM and O3 together. DISCUSSION:Epidemiologic and experimental studies provide some evidence for O3 concentration modifying the effect of PM, although PM did not modify O3 risk estimates. Experimental studies of combined exposure to PM and O3 provided evidence for additivity, synergism, and/or antagonism depending on the specific health endpoint. Evidence for other pollutant pairs was more limited. CONCLUSIONS:Overall, the evidence for multipollutant effects was often heterogeneous, and the limited number of studies inhibited making a conclusion about the nature of the relationship between pollutant combinations and cardiovascular disease.
Project description:Few studies have been conducted on the effect of air pollution on morbidity in Latin America. This study analyzed the effects of air pollution on respiratory and circulatory morbidity in four major cities in Colombia. An ecological time-series analysis was conducted with pollution data from air quality monitoring networks and information on emergency department visits between 2011 and 2014. Daily 24-h averages were calculated for NO?, PM10, PM2.5, and SO? as well as 8-h averages for CO and O?. Separate time-series were constructed by disease group and pollutant. Conditional negative binomial regression models were used with average population effects. Effects were calculated for the same day and were adjusted for weather conditions, age groups, and their interactions. The results showed that effects of some of the pollutants differed among the cities. For NO?, PM10, and PM2.5, the multi-city models showed greater and statistically significant percentage increases in emergency department visits for respiratory diseases, particularly for the 5 to 9-year-old age group. These same pollutants also significantly affected the rate of emergency department visits for circulatory diseases, especially for the group of persons over 60 years of age.
Project description:PURPOSE:Air pollution epidemiology traditionally focuses on the relationship between individual air pollutants and health outcomes (e.g., mortality). To account for potential copollutant confounding, individual pollutant associations are often estimated by adjusting or controlling for other pollutants in the mixture. Recently, the need to characterize the relationship between health outcomes and the larger multipollutant mixture has been emphasized in an attempt to better protect public health and inform more sustainable air quality management decisions. METHODS:New and innovative statistical methods to examine multipollutant exposures were identified through a broad literature search, with a specific focus on those statistical approaches currently used in epidemiologic studies of short-term exposures to criteria air pollutants (i.e., particulate matter, carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone). RESULTS:Five broad classes of statistical approaches were identified for examining associations between short-term multipollutant exposures and health outcomes, specifically additive main effects, effect measure modification, unsupervised dimension reduction, supervised dimension reduction, and nonparametric methods. These approaches are characterized including advantages and limitations in different epidemiologic scenarios. DISCUSSION:By highlighting the characteristics of various studies in which multipollutant statistical methods have been used, this review provides epidemiologists and biostatisticians with a resource to aid in the selection of the most optimal statistical method to use when examining multipollutant exposures.
Project description:Air pollution epidemiology continues moving toward the study of mixtures and multipollutant modeling. Simultaneously, there is a movement in epidemiology to estimate policy-relevant health effects that can be understood in reference to specific interventions. Scaling regression coefficients from a regression model by an interquartile range (IQR) is one common approach to presenting multipollutant health effect estimates. We are unaware of guidance on how to interpret these effect estimates as an intervention. To illustrate the issues of interpretability of IQR-scaled air pollution health effects, we analyzed how daily concentration changes in 2 air pollutants (nitrogen dioxide and particulate matter with aerodynamic diameter ? 2.5 ?m) related to one another within 2 seasons (summer and winter), within 3 cities with distinct air pollution profiles (Burbank, California; Houston, Texas; and Pittsburgh, Pennsylvania). In each city season, we examined how realistically IQR scaling in multipollutant lag-1 time-series studies reflects a hypothetical intervention that is possible given the observed data. We proposed 2 causal conditions to explicitly link IQR-scaled effects to a clearly defined hypothetical intervention. Condition 1 specified that the index pollutant had to experience a daily concentration change of greater than 1 IQR, reflecting the notion that the IQR is an appropriate measure of variability between consecutive days. Condition 2 specified that the copollutant had to remain relatively constant. We found that in some city seasons, there were very few instances in which these conditions were satisfied (eg, 1 day in Pittsburgh during summer). We discuss the practical implications of IQR scaling and suggest alternative approaches to presenting multipollutant effects that are supported by empirical data.
Project description:Various temporal metrics of daily pollution levels have been used to examine the relationships between air pollutants and acute health outcomes. However, daily metrics of the same pollutant have rarely been systematically compared within a study. In this analysis, we describe the variability of effect estimates attributable to the use of different temporal metrics of daily pollution levels. We obtained hourly measurements of ambient particulate matter (PM?.?), carbon monoxide (CO), nitrogen dioxide (NO?), and ozone (O?) from air monitoring networks in 20-county Atlanta for the time period 1993-2004. For each pollutant, we created (1) a daily 1-h maximum; (2) a 24-h average; (3) a commute average; (4) a daytime average; (5) a nighttime average; and (6) a daily 8-h maximum (only for O?). Using Poisson generalized linear models, we examined associations between daily counts of respiratory emergency department visits and the previous day's pollutant metrics. Variability was greatest across O? metrics, with the 8-h maximum, 1-h maximum, and daytime metrics yielding strong positive associations and the nighttime O? metric yielding a negative association (likely reflecting confounding by air pollutants oxidized by O?). With the exception of daytime metric, all of the CO and NO? metrics were positively associated with respiratory emergency department visits. Differences in observed associations with respiratory emergency room visits among temporal metrics of the same pollutant were influenced by the diurnal patterns of the pollutant, spatial representativeness of the metrics, and correlation between each metric and copollutant concentrations. Overall, the use of metrics based on the US National Ambient Air Quality Standards (for example, the use of a daily 8-h maximum O? as opposed to a 24-h average metric) was supported by this analysis. Comparative analysis of temporal metrics also provided insight into underlying relationships between specific air pollutants and respiratory health.
Project description:BACKGROUND:Air pollution has been related to brain structural alterations, but a relationship with white matter microstructure is unclear. OBJECTIVES:We assessed whether pregnancy and childhood exposures to air pollution are related to white matter microstructure in preadolescents. METHODS:We used data of 2,954 children from the Generation R Study, a population-based birth cohort from Rotterdam, Netherlands (2002-2006). Concentrations of 17 air pollutants including nitrogen oxides (NOX), particulate matter (PM), and components of PM were estimated at participants' homes during pregnancy and childhood using land-use regression models. Diffusion tensor images were obtained at child's 9-12 years of age, and fractional anisotropy (FA) and mean diffusivity (MD) were computed. We performed linear regressions adjusting for socioeconomic and lifestyle characteristics. Single-pollutant analyses were followed by multipollutant analyses using the Deletion/Substitution/Addition (DSA) algorithm. RESULTS:In the single-pollutant analyses, higher concentrations of several air pollutants during pregnancy or childhood were associated with significantly lower FA or higher MD (p<0.05). In multipollutant models of pregnancy exposures selected by DSA, higher concentration of fine particles was associated with significantly lower FA [-0.71 (95% CI: -1.26, -0.16) per 5??g/m3 fine particles] and higher concentration of elemental silicon with significantly higher MD [0.06 (95% CI: 0.01, 0.11) per 100?ng/m3 silicon]. Multipollutant models of childhood exposures selected by DSA indicated significant associations of NOX with FA [-0.14 (95% CI: -0.23, -0.04) per 20-?g/m3 NOX increase], and of elemental zinc and the oxidative potential of PM with MD [0.03 (95% CI: 0.01, 0.04) per 10-ng/m3 zinc increase and 0.07 (95% CI: 0.00, 0.44) per 1-nmol?DTT/min/m3 oxidative potential increase]. Mutually adjusted models of significant exposures during pregnancy and childhood indicated significant associations of silicon during pregnancy, and zinc during childhood, with MD. DISCUSSION:Exposure in pregnancy and childhood to air pollutants from tailpipe and non-tailpipe emissions were associated with lower FA and higher MD in white matter of preadolescents. https://doi.org/10.1289/EHP4709.
Project description:In time-series studies of ambient air pollution and health in large urban areas, measurement errors associated with instrument precision and spatial variability vary widely across pollutants. In this paper, we characterize these errors for selected air pollutants and estimate their impacts on epidemiologic results from an ongoing study of air pollution and emergency department visits in Atlanta. Error was modeled for daily measures of 12 air pollutants using collocated monitor data to characterize instrument precision and data from multiple study area monitors to estimate population-weighted spatial variance. Time-series simulations of instrument and spatial error were generated for each pollutant, added to a reference pollutant time-series, and used in a Poisson generalized linear model of air pollution and cardiovascular emergency department visits. Reductions in risk ratio due to instrument precision error were less than 6%. Error due to spatial variability resulted in average risk ratio reductions of less than 16% for secondary pollutants (O(3), PM(2.5) sulfate, nitrate and ammonium) and between 43% and 68% for primary pollutants (NO(x), NO(2), SO(2), CO, PM(2.5) elemental carbon); pollutants of mixed origin (PM(10), PM(2.5), PM(2.5) organic carbon) had intermediate impacts. Quantifying impacts of measurement error on health effect estimates improves interpretation across ambient pollutants.