Identifying groups of children's social mobility opportunity for public health applications using k-means clustering.

Background: The Opportunity Atlas project is a pioneering effort to trace social mobility and adulthood socioeconomic outcomes back to childhood residence. Half of the variation in adulthood socioeconomic outcomes was explainable by neighborhood-level socioeconomic characteristics during childhood. Clustering census tracts by Opportunity Atlas characteristics would allow for further exploration of variance in social mobility. Our objectives here are to identify and describe spatial clustering trends within Opportunity Atlas outcomes. Methods: We utilized a k-means clustering machine learning approach with four outcome variables (individual income, incarceration rate, employment, and percent of residents living in a neighborhood with low levels of poverty) each given at five parental income levels (1st, 25th, 50th, 75th, and 100th percentiles of the national distribution) to create clusters of census tracts across the contiguous United States (US) and within each Environmental Protection Agency region. Results: At the national level, the algorithm identified seven distinct clusters; the highest opportunity clusters occurred in the Northern Midwest and Northeast, and the lowest opportunity clusters occurred in rural areas of the Southwest and Southeast. For regional analyses, we identified between five to nine clusters within each region. PCA loadings fluctuate across parental income levels; income and low poverty neighborhood residence explain a substantial amount of variance across all variables, but there are differences in contributions across parental income levels for many components. Conclusions: Using data from the Opportunity Atlas, we have taken four social mobility opportunity outcome variables each stratified at five parental income levels and created nationwide and EPA region-specific clusters that group together census tracts with similar opportunity profiles. The development of clusters that can serve as a combined index of social mobility opportunity is an important contribution of this work, and this in turn can be employed in future investigations of factors associated with children's social mobility.

Evaluation of a Saccharomyces cerevisiae Fermentation Product on the Feedlot Performance and Carcass Merit of Hair Lambs Offered an Annual Ryegrass-Hay-Based Finishing Diet: A Pilot Study.

Hair sheep production has increased in recent years, which has resulted in an increased presence in feedlots. Additionally, grass-based finishing systems for ruminant animal production have increased. Data are limited for finishing hair lambs on diets based on cool-season hay. The objective was to evaluate a Saccharomyces cerevisiae fermentation product (SCFP) on the feedlot performance and carcass characteristics of Katahdin lambs offered an annual ryegrass (Lolium multiflorum)-hay-based diet. Twenty-four Katahdin lambs (21.5 ± 2.5 kg BW) were assigned to either the control (CON) or the yeast-supplemented group (SCFP) in a completely randomized design. Lambs were offered a 14% crude protein total mixed ration diet based on annual ryegrass hay. The SCFP group also received the yeast supplement in their diet. Lambs in the SCFP group had a higher final body weight (p < 0.01) and ADG (p = 0.01). Less maximum and total energy were required to cut SCFP lamb meat compared to CON lamb meat (p < 0.03). Results from this study indicated that SCFP supplementation may prove to be beneficial in hair lamb finishing diets. Future research will need to specifically evaluate the use of these products in hair lambs with a larger sample size.

Findings From Implementing a Patient Experience Survey in a Quality Measurement System for Substance Abuse Disorder Treatment Facilities in 6 States.

OBJECTIVES: Patient experience surveys (PESs) are an important component of determining the quality of health care. There is an absence of PES data available to people seeking to identify higher quality substance use disorder treatment providers. Our project aimed to correct this by implementing a PES for substance use disorder treatment providers and publicly disseminating PES information. METHODS: We created a population frame of all addiction providers in 6 states. Providers were asked to disseminate a survey invitation letter directing patients to a survey Web site. No personally identifiable information was exchanged. We developed a 10-question survey, reflecting characteristics National Institute on Drug Abuse (NIDA), National Institute on Alcohol Abuse and Alcoholism (NIAAA), Substance Abuse and Mental Health Services Administration (SAMHSA) have identified as reflecting higher-quality addiction treatment. RESULTS: Nineteen percent of facilities participated; among participating facilities, 9627 patients completed the survey. Patient experience varied significantly by facility with the percentage of a facility's patients who chose the most positive answer varying widely. We calculated that between-facility reliability will meet or exceed 0.80 for facilities with 20 or more responding patients. We searched for but did not find evidence of data falsification. CONCLUSIONS: This cost-efficient survey protocol is low burden for providers and patients. Results suggest significant differences in quality of care among facilities, and facility-level results are important to provide to consumers when they evaluate the relative patient-reported quality of facilities. The data are not designed to provide population-based statistics. As more facilities and patients per facility participate, public-facing PES data will be increasingly useful to consumers seeking to compare and choose facilities.

Development of the United States Environmental Protection Agency's Facilities Status Dashboard for the COVID-19 Pandemic: Approach and Challenges.

Objectives: Develop a tool for applying various COVID-19 re-opening guidelines to the more than 120 U.S. Environmental Protection Agency (EPA) facilities. Methods: A geographic information system boundary was created for each EPA facility encompassing the county where the EPA facility is located and the counties where employees commuted from. This commuting area is used for display in the Dashboard and to summarize population and COVID-19 health data for analysis. Results: Scientists in EPA's Office of Research and Development developed the EPA Facility Status Dashboard, an easy-to-use web application that displays data and statistical analyses on COVID-19 cases, testing, hospitalizations, and vaccination rates. Conclusion: The Dashboard was designed to provide readily accessible information for EPA management and staff to view and understand the COVID-19 risk surrounding each facility. It has been modified several times based on user feedback, availability of new data sources, and updated guidance. The views expressed in this article are those of the authors and do not necessarily represent the views or the policies of the U.S. Environmental Protection Agency.

Evaluating Canada's single-use plastic mitigation policies via brand audit and beach cleanup data to reduce plastic pollution.

Single-use plastics (SUPs) represent a major threat to marine environments and require proactive policies to reduce consumption and mismanagement. Many SUP management strategies exist to reduce SUP use and mitigate environmental impacts, including extended producer responsibility (EPR), deposit-return schemes, SUP bans or taxes, and public outreach and education. This study analyzed brand audit and beach cleanup data in four densely populated Canadian cities (Vancouver, Toronto, Montréal, Halifax) and a remote island (Sable Island) to determine efficacy of ongoing SUP mitigation measures. Cities were found to have similar litter type proportions, and six brands were found to disproportionally contribute to Canadian SUP litter, comprising 39% of branded litter collected. Results confirm that current Canadian SUP management appears to be insufficient to address leakage of SUPs into the environment. Recommendations to strengthen SUP management strategies and mitigate plastic pollution are recommended to improve future Canadian SUP reduction policies.

Cover Crop Management on the Southern High Plains: Impacts on Crop Productivity and Soil Water Depletion.

The imminent depletion of the Ogallala Aquifer demands innovative cropping alternatives. Even though the benefits of cover crops are well recognized, adoption has been slow in the Southern High Plains (SHP) of the United States because of concerns that cover crops withdraw soil water to the detriment of the summer crops. This small plot experiment tested the interacting effects-production, soil water depletion of the cover crops, and subsequent teff [Eragrostis tef (Zucc.) Trotter] summer hay crops-of irrigation and tillage management with five cover crop types to identify low-risk cover crop practices in the drought-prone SHP. Dryland rye (Secale cereale L.) produced modest forage biomass (>1000 kg ha-1), even in a dry year, but it was found that light irrigation should be used to ensure adequate forage supply (>1200 kg ha-1) if winter grazing is desired. No-till management and timely termination of the winter cover crops were crucial to reducing the negative impact of winter crops on summer teff production. The results indicated no detriment to soil water content that was attributable to planting no-till cover crops compared with the conventional practice of winter fallow. Therefore, producers could take advantage of the soil-conserving attributes of high-quality winter forage cover crops without experiencing significant soil water depletion.

Exploration of PM mass, source, and component-related factors that might explain heterogeneity in daily PM2.5-mortality associations across the United States.

Multi-city epidemiologic studies examining short-term (daily) differences in fine particulate matter (PM2.5) provide evidence of substantial spatial heterogeneity in city-specific mortality risk estimates across the United States. Because PM2.5 is a mixture of particles, both directly emitted from sources or formed through atmospheric reactions, some of this heterogeneity may be due to regional variations in PM2.5 toxicity. Using inverse variance weighted linear regression, we examined change in percent change in mortality in association with 24 "exposure" determinants representing three basic groupings based on potential explanations for differences in PM toxicity - size, source, and composition. Percent changes in mortality for the PM2.5-mortality association for 313 core-based statistical areas and their metropolitan divisions over 1999-2005 were used as the outcome. Several determinants were identified as potential contributors to heterogeneity: all mass fraction determinants, vehicle miles traveled (VMT) for diesel total, VMT gas per capita, PM2.5 ammonium, PM2.5 nitrate, and PM2.5 sulfate. In multivariable models, only daily correlation of PM2.5 with PM10 and long-term average PM2.5 mass concentration were retained, explaining approximately 10% of total variability. The results of this analysis contribute to the growing body of literature specifically focusing on assessing the underlying basis of the observed spatial heterogeneity in PM2.5-mortality effect estimates, continuing to demonstrate that this heterogeneity is multifactorial and not attributable to a single aspect of PM.

Human exposure factors as potential determinants of the heterogeneity in city-specific associations between PM2.5 and mortality.

Multi-city population-based epidemiological studies of short-term fine particulate matter (PM2.5) exposures and mortality have observed heterogeneity in risk estimates between cities. Factors affecting exposures, such as pollutant infiltration, which are not captured by central-site monitoring data, can differ between communities potentially explaining some of this heterogeneity. This analysis evaluates exposure factors as potential determinants of the heterogeneity in 312 core-based statistical areas (CBSA)-specific associations between PM2.5 and mortality using inverse variance weighted linear regression. Exposure factor variables were created based on data on housing characteristics, commuting patterns, heating fuel usage, and climatic factors from national surveys. When survey data were not available, air conditioning (AC) prevalence was predicted utilizing machine learning techniques. Across all CBSAs, there was a 0.95% (Interquartile range (IQR) of 2.25) increase in non-accidental mortality per 10 µg/m3 increase in PM2.5 and significant heterogeneity between CBSAs. CBSAs with larger homes, more heating degree days, a higher percentage of home heating with oil had significantly (p < 0.05) higher health effect estimates, while cities with more gas heating had significantly lower health effect estimates. While univariate models did not explain much of heterogeneity in health effect estimates (R2 < 1%), multivariate models began to explain some of the observed heterogeneity (R2 = 13%).

Influence of exposure differences on city-to-city heterogeneity in PM2.5-mortality associations in US cities.

BACKGROUND: Multi-city population-based epidemiological studies have observed heterogeneity between city-specific fine particulate matter (PM2.5)-mortality effect estimates. These studies typically use ambient monitoring data as a surrogate for exposure leading to potential exposure misclassification. The level of exposure misclassification can differ by city affecting the observed health effect estimate. METHODS: The objective of this analysis is to evaluate whether previously developed residential infiltration-based city clusters can explain city-to-city heterogeneity in PM2.5 mortality risk estimates. In a prior paper 94 cities were clustered based on residential infiltration factors (e.g. home age/size, prevalence of air conditioning (AC)), resulting in 5 clusters. For this analysis, the association between PM2.5 and all-cause mortality was first determined in 77 cities across the United States for 2001-2005. Next, a second stage analysis was conducted evaluating the influence of cluster assignment on heterogeneity in the risk estimates. RESULTS: Associations between a 2-day (lag 0-1 days) moving average of PM2.5 concentrations and non-accidental mortality were determined for each city. Estimated effects ranged from -3.2 to 5.1% with a pooled estimate of 0.33% (95% CI: 0.13, 0.53) increase in mortality per 10 µg/m3 increase in PM2.5. The second stage analysis determined that cluster assignment was marginally significant in explaining the city-to-city heterogeneity. The health effects estimates in cities with older, smaller homes with less AC (Cluster 1) and cities with newer, smaller homes with a large prevalence of AC (Cluster 3) were significantly lower than the cluster consisting of cities with older, larger homes with a small percentage of AC. CONCLUSIONS: This is the first study that attempted to examine whether multiple exposure factors could explain the heterogeneity in PM2.5-mortality associations. The results of this study were found to explain a small portion (6%) of this heterogeneity.

Probabilistic estimation of residential air exchange rates for population-based human exposure modeling.

Residential air exchange rates (AERs) are a key determinant in the infiltration of ambient air pollution indoors. Population-based human exposure models using probabilistic approaches to estimate personal exposure to air pollutants have relied on input distributions from AER measurements. An algorithm for probabilistically estimating AER was developed based on the Lawrence Berkley National Laboratory Infiltration model utilizing housing characteristics and meteorological data with adjustment for window opening behavior. The algorithm was evaluated by comparing modeled and measured AERs in four US cities (Los Angeles, CA; Detroit, MI; Elizabeth, NJ; and Houston, TX) inputting study-specific data. The impact on the modeled AER of using publically available housing data representative of the region for each city was also assessed. Finally, modeled AER based on region-specific inputs was compared with those estimated using literature-based distributions. While modeled AERs were similar in magnitude to the measured AER they were consistently lower for all cities except Houston. AERs estimated using region-specific inputs were lower than those using study-specific inputs due to differences in window opening probabilities. The algorithm produced more spatially and temporally variable AERs compared with literature-based distributions reflecting within- and between-city differences, helping reduce error in estimates of air pollutant exposure.

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models.

BACKGROUND: Exposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health. METHODS: ZIP-code level estimates of exposure for six pollutants (CO, NOx, EC, PM2.5, SO4, O3) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error. Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs. RESULTS: Substantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3-85% for population error, and 31-85% for total error. When CO, NOx or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copollutants based on the estimated type I error rate. CONCLUSIONS: The impact of exposure error must be considered when interpreting results of copollutant epidemiologic models, due to the possibility of attenuation of main pollutant RRs and the increased probability of false positives when measurement error is present.

Comparing multipollutant emissions-based mobile source indicators to other single pollutant and multipollutant indicators in different urban areas.

A variety of single pollutant and multipollutant metrics can be used to represent exposure to traffic pollutant mixtures and evaluate their health effects. Integrated mobile source indicators (IMSIs) that combine air quality concentration and emissions data have recently been developed and evaluated using data from Atlanta, Georgia. IMSIs were found to track trends in traffic-related pollutants and have similar or stronger associations with health outcomes. In the current work, we apply IMSIs for gasoline, diesel and total (gasoline + diesel) vehicles to two other cities (Denver, Colorado and Houston, Texas) with different emissions profiles as well as to a different dataset from Atlanta. We compare spatial and temporal variability of IMSIs to single-pollutant indicators (carbon monoxide (CO), nitrogen oxides (NOx) and elemental carbon (EC)) and multipollutant source apportionment factors produced by Positive Matrix Factorization (PMF). Across cities, PMF-derived and IMSI gasoline metrics were most strongly correlated with CO (r = 0.31-0.98), while multipollutant diesel metrics were most strongly correlated with EC (r = 0.80-0.98). NOx correlations with PMF factors varied across cities (r = 0.29-0.67), while correlations with IMSIs were relatively consistent (r = 0.61-0.94). In general, single-pollutant metrics were more correlated with IMSIs (r = 0.58-0.98) than with PMF-derived factors (r = 0.07-0.99). A spatial analysis indicated that IMSIs were more strongly correlated (r > 0.7) between two sites in each city than single pollutant and PMF factors. These findings provide confidence that IMSIs provide a transferable, simple approach to estimate mobile source air pollution in cities with differing topography and source profiles using readily available data.

An empirical assessment of exposure measurement error and effect attenuation in bipollutant epidemiologic models.

BACKGROUND: Using multipollutant models to understand combined health effects of exposure to multiple pollutants is becoming more common. However, complex relationships between pollutants and differing degrees of exposure error across pollutants can make health effect estimates from multipollutant models difficult to interpret. OBJECTIVES: We aimed to quantify relationships between multiple pollutants and their associated exposure errors across metrics of exposure and to use empirical values to evaluate potential attenuation of coefficients in epidemiologic models. METHODS: We used three daily exposure metrics (central-site measurements, air quality model estimates, and population exposure model estimates) for 193 ZIP codes in the Atlanta, Georgia, metropolitan area from 1999 through 2002 for PM2.5 and its components (EC and SO4), as well as O3, CO, and NOx, to construct three types of exposure error: δspatial (comparing air quality model estimates to central-site measurements), δpopulation (comparing population exposure model estimates to air quality model estimates), and δtotal (comparing population exposure model estimates to central-site measurements). We compared exposure metrics and exposure errors within and across pollutants and derived attenuation factors (ratio of observed to true coefficient for pollutant of interest) for single- and bipollutant model coefficients. RESULTS: Pollutant concentrations and their exposure errors were moderately to highly correlated (typically, > 0.5), especially for CO, NOx, and EC (i.e., "local" pollutants); correlations differed across exposure metrics and types of exposure error. Spatial variability was evident, with variance of exposure error for local pollutants ranging from 0.25 to 0.83 for δspatial and δtotal. The attenuation of model coefficients in single- and bipollutant epidemiologic models relative to the true value differed across types of exposure error, pollutants, and space. CONCLUSIONS: Under a classical exposure-error framework, attenuation may be substantial for local pollutants as a result of δspatial and δtotal with true coefficients reduced by a factor typically < 0.6 (results varied for δpopulation and regional pollutants).

Evaluating the application of multipollutant exposure metrics in air pollution health studies.

BACKGROUND: Health effects associated with air pollution are typically evaluated using a single pollutant approach, yet people are exposed to mixtures consisting of multiple pollutants that may have independent or combined effects on human health. Development of exposure metrics that represent the multipollutant environment is important to understand the impact of ambient air pollution on human health. OBJECTIVES: We reviewed existing multipollutant exposure metrics to evaluate how they can be applied to understand associations between air pollution and health effects. METHODS: We conducted a literature search using both targeted search terms and a relational search in Web of Science and PubMed in April and December 2013. We focused on exposure metrics that are constructed from ambient pollutant concentrations and can be broadly applied to evaluate air pollution health effects. RESULTS: Multipollutant exposure metrics were identified in 57 eligible studies. Metrics reviewed can be categorized into broad pollutant grouping paradigms based on: 1) source emissions and atmospheric processes or 2) common health outcomes. DISCUSSION: When comparing metrics, it is apparent that no universal exposure metric exists; each type of metric addresses different research questions and provides unique information on human health effects. Key limitations of these metrics include the balance between complexity and simplicity as well as the lack of an existing "gold standard" for multipollutant health effects and exposure. CONCLUSIONS: Future work on characterizing multipollutant exposure error and joint effects will inform development of improved multipollutant metrics to advance air pollution health effects research and human health risk assessment.

Clustering cities with similar fine particulate matter exposure characteristics based on residential infiltration and in-vehicle commuting factors.

Epidemiological studies have observed between city heterogeneity in PM2.5-mortality risk estimates. These differences could potentially be due to the use of central-site monitors as a surrogate for exposure which do not account for an individual's activities or ambient pollutant infiltration to the indoor environment. Therefore, relying solely on central-site monitoring data introduces exposure error in the epidemiological analysis. The amount of exposure error produced by using the central-site monitoring data may differ by city. The objective of this analysis was to cluster cities with similar exposure distributions based on residential infiltration and in-vehicle commuting characteristics. Factors related to residential infiltration and commuting were developed from the American Housing Survey (AHS) from 2001 to 2005 for 94 Core-Based Statistical Areas (CBSAs). We conducted two separate cluster analyses using a k-means clustering algorithm to cluster CBSAs based on these factors. The first only included residential infiltration factors (i.e. percent of homes with central air conditioning (AC) mean year home was built, and mean home size) while the second incorporated both infiltration and commuting (i.e. mean in-vehicle commuting time and mean in-vehicle commuting distance) factors. Clustering on residential infiltration factors resulted in 5 clusters, with two having distinct exposure distributions. Cluster 1 consisted of cities with older, smaller homes with less central AC while homes in Cluster 2 cities were newer, larger, and more likely to have central AC. Including commuting factors resulted in 10 clusters. Clusters with shorter in-vehicle commuting times had shorter in-vehicle commuting distances. Cities with newer homes also tended to have longer commuting times and distances. This is the first study to employ cluster analysis to group cities based on exposure factors. Identifying cities with similar exposure distributions may help explain city-to-city heterogeneity in PM2.5 mortality risk estimates.

Exposure prediction approaches used in air pollution epidemiology studies: key findings and future recommendations.

Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure variability in a study population, thus resulting in exposure error and biased estimates. Articles in this dedicated issue examine various approaches to predict or assign exposures to ambient pollutants. These methods include combining existing central-site pollution measurements with local- and/or regional-scale air quality models to create new or "hybrid" models for pollutant exposure estimates and using exposure models to account for factors such as infiltration of pollutants indoors and human activity patterns. Key findings from these articles are summarized to provide lessons learned and recommendations for additional research on improving exposure estimation approaches for future epidemiological studies. In summary, when compared with use of central-site monitoring data, the enhanced spatial resolution of air quality or exposure models can have an impact on resultant health effect estimates, especially for pollutants derived from local sources such as traffic (e.g., EC, CO, and NO(x)). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develops pollutant-specific infiltration data (including for PM species) and improves existing data on human time-activity patterns and exposure to local source (e.g., traffic), in order to enhance human exposure modeling estimates. We also recommend comparing how various approaches to exposure estimation characterize relationships between multiple pollutants in time and space and investigating the impact of improved exposure estimates in chronic health studies.

Development and evaluation of alternative approaches for exposure assessment of multiple air pollutants in Atlanta, Georgia.

Measurements from central site (CS) monitors are often used as estimates of exposure in air pollution epidemiological studies. As these measurements are typically limited in their spatiotemporal resolution, true exposure variability within a population is often obscured, leading to potential measurement errors. To fully examine this limitation, we developed a set of alternative daily exposure metrics for each of the 169 ZIP codes in the Atlanta, GA, metropolitan area, from 1999 to 2002, for PM(2.5) and its components (elemental carbon (EC), SO(4)), O(3), carbon monoxide (CO), and nitrogen oxides (NOx). Metrics were applied in a study investigating the respiratory health effects of these pollutants. The metrics included: (i) CS measurements (one CS per pollutant); (ii) air quality model results for regional background pollution; (iii) local-scale AERMOD air quality model results; (iv) hybrid air quality model estimates (a combination of (ii) and (iii)); and (iv) population exposure model predictions (SHEDS and APEX). Differences in estimated spatial and temporal variability were compared by exposure metric and pollutant. Comparisons showed that: (i) both hybrid and exposure model estimates exhibited high spatial variability for traffic-related pollutants (CO, NO(x), and EC), but little spatial variability among ZIP code centroids for regional pollutants (PM(2.5), SO(4), and O(3)); (ii) for all pollutants except NO(x), temporal variability was consistent across metrics; (iii) daily hybrid-to-exposure model correlations were strong (r>0.82) for all pollutants, suggesting that when temporal variability of pollutant concentrations is of main interest in an epidemiological application, the use of estimates from either model may yield similar results; (iv) exposure models incorporating infiltration parameters, time-location-activity budgets, and other exposure factors affect the magnitude and spatiotemporal distribution of exposure, especially for local pollutants. The results of this analysis can inform the development of more appropriate exposure metrics for future epidemiological studies of the short-term effects of particulate and gaseous ambient pollutant exposure in a community.

The triggering of myocardial infarction by fine particles is enhanced when particles are enriched in secondary species.

Previous studies have reported an increased risk of myocardial infarction (MI) associated with acute increases in PM concentration. Recently, we reported that MI/fine particle (PM2.5) associations may be limited to transmural infarctions. In this study, we retained data on hospital discharges with a primary diagnosis of acute myocardial infarction (using International Classification of Diseases ninth Revision [ICD-9] codes), for those admitted January 1, 2004 to December 31, 2006, who were ≥ 18 years of age, and were residents of New Jersey at the time of their MI. We excluded MI with a diagnosis of a previous MI and MI coded as a subendocardial infarction, leaving n = 1563 transmural infarctions available for analysis. We coupled these health data with PM2.5 species concentrations predicted by the Community Multiscale Air Quality chemical transport model, ambient PM2.5 concentrations, and used the same case-crossover methods to evaluate whether the relative odds of transmural MI associated with increased PM2.5 concentration is modified by the PM2.5 composition/mixture (i.e., mass fractions of sulfate, nitrate, elemental carbon, organic carbon, and ammonium). We found the largest relative odds estimates on the days with the highest tertile of sulfate mass fraction (OR = 1.13; 95% CI = 1.00, 1.27), nitrate mass fraction (OR = 1.18; 95% CI = 0.98, 1.35), and ammonium mass fraction (OR = 1.13; 95% CI = 1.00 1.28), and the lowest tertile of EC mass fraction (OR = 1.17; 95% CI = 1.03, 1.34). Air pollution mixtures on these days were enhanced in pollutants formed through atmospheric chemistry (i.e., secondary PM2.5) and depleted in primary pollutants (e.g., EC). When mixtures were laden with secondary PM species (sulfate, nitrate, and/or organics), we observed larger relative odds of myocardial infarction associated with increased PM2.5 concentrations. Further work is needed to confirm these findings and examine which secondary PM2.5 component(s) is/are responsible for an acute MI response.

Spatiotemporally resolved air exchange rate as a modifier of acute air pollution-related morbidity in Atlanta.

Epidemiological studies frequently use central site concentrations as surrogates of exposure to air pollutants. Variability in air pollutant infiltration due to differential air exchange rates (AERs) is potentially a major factor affecting the relationship between central site concentrations and actual exposure, and may thus influence observed health risk estimates. In this analysis, we examined AER as an effect modifier of associations between several urban air pollutants and corresponding emergency department (ED) visits for asthma and wheeze during a 4-year study period (January 1999-December 2002) for a 186 ZIP code area in metro Atlanta. We found positive associations for the interaction between AER and pollution on asthma ED visits for both carbon monoxide (CO) and nitrogen oxides (NO(x)), indicating significant or near-significant effect modification by AER on the pollutant risk-ratio estimates. In contrast, the interaction term between particulate matter (PM)(2.5) and AER on asthma ED visits was negative and significant. However, alternative distributional tertile analyses showed PM(2.5) and AER epidemiological model results to be similar to those found for NOx and CO (namely, increasing risk ratios (RRs) with increasing AERs when ambient PM(2.5) concentrations were below the highest tertile of their distribution). Despite the fact that ozone (O(3)) was a strong independent predictor of asthma ED visits in our main analysis, we found no O(3)-AER effect modification. To our knowledge, our findings for CO, NOx, and PM(2.5) are the first to provide an indication of short-term (i.e., daily) effect modification of multiple air pollution-related risk associations with daily changes in AER. Although limited to one outcome category in a single large urban locale, the findings suggest that the use of relatively simple and easy-to-derive AER surrogates may reflect intraurban differences in short-term exposures to pollutants of ambient origin.