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.

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.

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.

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.

Assessing health care students' intentions and motivations for learning: the Healthcare Learning and Studying Inventory (HLSI).

Inventories that measure approaches to learning have revealed that certain approaches are associated with better academic performance. However, these inventories were developed primarily with higher education students on non-vocational courses and recent research shows they fail to capture the full range of healthcare students' intentions and motivations for learning. To develop a new inventory measuring approaches to learning that addresses these shortfalls and is relevant to students on vocational courses in healthcare. In depth interviews with healthcare students were performed to understand the full range of healthcare students' intentions and motivations. The data were used to create a draft inventory, which was reviewed by interview participants and then tested with medical students. The final inventory was piloted with 303 healthcare students across six disciplines. Exploratory factor analysis was used to identify groups of related items within the inventory. The research produced a 32 item scale based on rich qualitative data, with a four factor structure and good internal consistency. A desire to link theory and practice was a distinctive feature of healthcare students. The new inventory contains nuanced items that enable a better understanding of their common and distinctive intentions and motivations. This study suggests that healthcare student populations have some unique intentions and motivations for learning and therefore require a bespoke inventory to ensure that important aspects are not missed. It offers a new tool for meaningful future research, the Healthcare Learning and Studying Inventory (HLSI).

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.

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.

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.

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.

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.

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.

Evaluating heterogeneity in indoor and outdoor air pollution using land-use regression and constrained factor analysis.

Previous studies have identified associations between traffic exposures and a variety of adverse health effects, but many of these studies relied on proximity measures rather than measured or modeled concentrations of specific air pollutants, complicating interpretability of the findings. An increasing number of studies have used land-use regression (LUR) or other techniques to model small-scale variability in concentrations of specific air pollutants. However, these studies have generally considered a limited number of pollutants, focused on outdoor concentrations (or indoor concentrations of ambient origin) when indoor concentrations are better proxies for personal exposures, and have not taken full advantage of statistical methods for source apportionment that may have provided insight about the structure of the LUR models and the interpretability of model results. Given these issues, the primary objective of our study was to determine predictors of indoor and outdoor residential concentrations of multiple traffic-related air pollutants within an urban area, based on a combination of central site monitoring data; geographic information system (GIS) covariates reflecting traffic and other outdoor sources; questionnaire data reflecting indoor sources and activities that affect ventilation rates; and factor-analytic methods to better infer source contributions. As part of a prospective birth cohort study assessing asthma etiology in urban Boston, we collected indoor and/or outdoor 3-to-4 day samples of nitrogen dioxide (NO2) and fine particulate matter with an aerodynamic diameter or = 2.5 pm (PM2.5) at 44 residences during multiple seasons of the year from 2003 through 2005. We performed reflectance analysis, x-ray fluorescence spectroscopy (XRF), and high-resolution inductively coupled plasma-mass spectrometry (ICP-MS) on particle filters to estimate the concentrations of elemental carbon (EC), trace elements, and water-soluble metals, respectively. We derived multiple indicators of traffic using Massachusetts Highway Department (MHD) data and traffic counts collected outside the residences where the air monitoring was conducted. We used a standardized questionnaire to collect data on home characteristics and occupant behaviors. Additional housing information was collected through property tax records. Ambient concentrations of pollutants as well as meteorological data were collected from centrally located ambient monitors. We used GIS-based LUR models to explain spatial and temporal variability in residential outdoor concentrations of PM2.5, EC, and NO2. We subsequently derived latent-source factors for residential outdoor concentrations using confirmatory factor analysis constrained to nonnegative loadings. We developed LUR models to determine whether GIS covariates and other predictors explain factor variability and thereby support initial factor interpretations. To evaluate indoor concentrations, we developed physically interpretable regression models that explored the relationship between measured indoor and outdoor concentrations, relying on questionnaire data to characterize indoor sources and activities. Because outdoor pollutant concentrations measured directly outside of homes are unlikely to be available for most large epidemiologic studies, we developed regression models to explain indoor concentrations of PM2.5, EC, and NO2 as a function of other, more readily available data: GIS covariates, questionnaire data reflecting both sources and ventilation, and central site monitoring data. As we did for outdoor concentrations, we then derived latent-source factors for residential indoor concentrations and developed regression models explaining variability in these indoor latent-source factors. Finally, to provide insight about the effects of improved characterization of exposures for the results of subsequent epidemiologic investigations, we developed a simulation framework to quantitatively compare the implications of using exposure models derived from validation studies with the use of other surrogate models with varying amounts of measurement error. The concentrations of outdoor PM2.5 were strongly associated with the central site monitor data, whereas EC concentrations showed greater spatial variability, especially during colder months, and were predicted by the length of roadway within 200 m of the home. Outdoor NO2 also showed significant spatial variability, predicted in part by population density and roadway length within 50 m of the home. Our constrained factor analysis of outdoor concentrations produced loadings indicating long-range transport, brake wear and traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust as sources; corresponding LUR models largely corroborated these factor interpretations through covariate significance. For example, long-range transport was predicted by central site PM2.5, and season, brake wear and traffic exhaust and resuspended road dust by traffic and residential density, diesel exhaust by the percentage of diesel traffic on the nearest major road, and fuel oil combustion by population density. Our modeling of the concentrations of indoor pollutants demonstrated substantial variability in indoor-outdoor relationships across constituents, helping to separate constituents dominated by outdoor sources (e.g., S, Se, and V) from those dominated by indoor sources (e.g., Ca and Si). Regression models indicated that indoor PM2.5 was not influenced substantially by local traffic but had significant indoor sources (cooking activity and occupant density), while EC was associated with distance to the nearest designated truck route, and NO2 was associated with both traffic density within 50 m of the home and gas stove usage. Our constrained factor analysis of indoor concentrations helped to separate outdoor-dominated factors from indoor-dominated factors, though some factors appeared to be influenced by both indoor and outdoor sources. Subsequent factor analyses of the indoor-attributable fractions from indoor-outdoor regression models provided generally consistent interpretations of indoor-dominated factors. The use of regression models on indoor factors demonstrated the limited predictive power of questionnaire data related to indoor sources, but reinforced the viability of modeling indoor concentrations of pollutants of ambient origin. In spite of the relatively weak predictive power of some of the indoor-concentration regression models, our epidemiologic simulations illustrated that exposure models with fairly modest R2 values (in the range of 0.3 through 0.4, corresponding with the regression models for PM2.5 and NO2) yielded substantial improvements in epidemiologic study performance relative to the use of exposure proxies that could be applied in the absence of validation studies. In spite of limitations related to sample size and available covariate data, our study demonstrated significant outdoor spatial variability within an urban area in NO2 and in several constituents of airborne particles. LUR techniques combined with constrained factor analysis helped to disentangle the contributions to temporal variability of local, long-range transport, and other sources, ultimately allowing exposures from defined source categories to be investigated in epidemiologic studies. For the indoor residential environment, we demonstrated substantial variability in indoor-outdoor relationships among particle constituents; then, using information from public databases and focused questionnaire data, we were able to predict indoor concentrations for a subset of key pollutants. Constrained factor analysis methods applied to the indoor environment helped to separate indoor sources from outdoor sources. The corresponding indoor regression models had limited predictive power, reinforcing the complexity of characterizing the indoor environment when only limited information about key predictors is available. This finding also underscores the likelihood that these regression models might characterize indoor concentrations of pollutants with ambient origins better than they can the indoor concentrations from all sources. Our findings provide direction for future studies characterizing indoor exposure sources and patterns, and our epidemiologic simulation reinforced the importance of reducing measurement error in a context where many traffic-related air pollutants are influenced by both indoor and outdoor sources. The combination of analytical techniques used in our study could ultimately allow for more refined exposure characterization and evaluation of the relative contributions of various sources to health outcomes in epidemiologic studies.

Uncertainty and variability in health-related damages from coal-fired power plants in the United States.

The health-related damages associated with emissions from coal-fired power plants can vary greatly across facilities as a function of plant, site, and population characteristics, but the degree of variability and the contributing factors have not been formally evaluated. In this study, we modeled the monetized damages associated with 407 coal-fired power plants in the United States, focusing on premature mortality from fine particulate matter (PM(2.5)). We applied a reduced-form chemistry-transport model accounting for primary PM(2.5) emissions and the influence of sulfur dioxide (SO(2)) and nitrogen oxide (NO(x)) emissions on secondary particulate formation. Outputs were linked with a concentration-response function for PM(2.5)-related mortality that incorporated nonlinearities and model uncertainty. We valued mortality with a value of statistical life approach, characterizing and propagating uncertainties in all model elements. At the median of the plant-specific uncertainty distributions, damages across plants ranged from $30,000 to $500,000 per ton of PM(2.5), $6,000 to $50,000 per ton of SO(2), $500 to $15,000 per ton of NO(x), and $0.02 to $1.57 per kilowatt-hour of electricity generated. Variability in damages per ton of emissions was almost entirely explained by population exposure per unit emissions (intake fraction), which itself was related to atmospheric conditions and the population size at various distances from the power plant. Variability in damages per kilowatt-hour was highly correlated with SO(2) emissions, related to fuel and control technology characteristics, but was also correlated with atmospheric conditions and population size at various distances. Our findings emphasize that control strategies that consider variability in damages across facilities would yield more efficient outcomes.

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%).

Land use regression modeling of intra-urban residential variability in multiple traffic-related air pollutants.

BACKGROUND: There is a growing body of literature linking GIS-based measures of traffic density to asthma and other respiratory outcomes. However, no consensus exists on which traffic indicators best capture variability in different pollutants or within different settings. As part of a study on childhood asthma etiology, we examined variability in outdoor concentrations of multiple traffic-related air pollutants within urban communities, using a range of GIS-based predictors and land use regression techniques. METHODS: We measured fine particulate matter (PM2.5), nitrogen dioxide (NO2), and elemental carbon (EC) outside 44 homes representing a range of traffic densities and neighborhoods across Boston, Massachusetts and nearby communities. Multiple three to four-day average samples were collected at each home during winters and summers from 2003 to 2005. Traffic indicators were derived using Massachusetts Highway Department data and direct traffic counts. Multivariate regression analyses were performed separately for each pollutant, using traffic indicators, land use, meteorology, site characteristics, and central site concentrations. RESULTS: PM2.5 was strongly associated with the central site monitor (R2 = 0.68). Additional variability was explained by total roadway length within 100 m of the home, smoking or grilling near the monitor, and block-group population density (R2 = 0.76). EC showed greater spatial variability, especially during winter months, and was predicted by roadway length within 200 m of the home. The influence of traffic was greater under low wind speed conditions, and concentrations were lower during summer (R2 = 0.52). NO2 showed significant spatial variability, predicted by population density and roadway length within 50 m of the home, modified by site characteristics (obstruction), and with higher concentrations during summer (R2 = 0.56). CONCLUSION: Each pollutant examined displayed somewhat different spatial patterns within urban neighborhoods, and were differently related to local traffic and meteorology. Our results indicate a need for multi-pollutant exposure modeling to disentangle causal agents in epidemiological studies, and further investigation of site-specific and meteorological modification of the traffic-concentration relationship in urban neighborhoods.

Hepatitis A 2004 vaccination in children: methods and findings of a survey in two states.

BACKGROUND: Hepatitis A vaccine coverage estimates needed for surveillance and vaccine policy decisions are not readily available for children older than 35 months or for adolescents. This article reports methodology developed for obtaining such estimates by telephone survey with and without provider record verification. METHODS: A random-digit-dial telephone survey with provider verification was conducted in Arizona and Oregon in 2004-2005 to obtain coverage estimates for children aged 2.5 to 15 years based on parental reports from telephone survey data alone, and from multiple logistic regressions using both telephone survey and provider data. Analysis was performed during 2006. RESULTS: Vaccination information was collected from parents of 1266 children, and provider verification from 488. Telephone survey and provider record-based hepatitis A vaccine coverage (one or more doses) was 60% and 65%, respectively, in Arizona, and 39% and 26%, respectively, in Oregon. Children who were younger, lived in metropolitan areas, or were Hispanic or nonwhite had significantly higher coverage; parents with immunization records provided more-accurate information. While a logistic model-based estimator developed using both parent and provider data performed slightly better than the estimator based on parent data alone, they differed mostly in the subgroups that had small sample sizes. CONCLUSIONS: These are the first statewide provider-verified hepatitis A vaccine coverage estimates for children older than 35 months and indicate that telephone survey estimates as developed using this methodology could prove useful for immunization surveillance activities if interpreted cautiously.

Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data.

Previous studies have identified associations between traffic-related air pollution and adverse health effects. Most have used measurements from a few central ambient monitors and/or some measure of traffic as indicators of exposure, disregarding spatial variability and/or factors influencing personal exposure-ambient concentration relationships. This study seeks to utilize publicly available data (i.e., central site monitors, geographic information system (GIS), and property assessment data) and questionnaire responses to predict residential indoor concentrations of traffic-related air pollutants for lower socioeconomic status (SES) urban households.As part of a prospective birth cohort study in urban Boston, we collected indoor and outdoor 3-4 day samples of nitrogen dioxide (NO(2)) and fine particulate matter (PM(2.5)) in 43 low SES residences across multiple seasons from 2003 - 2005. Elemental carbon concentrations were determined via reflectance analysis. Multiple traffic indicators were derived using Massachusetts Highway Department data and traffic counts collected outside sampling homes. Home characteristics and occupant behaviors were collected via a standardized questionnaire. Additional housing information was collected through property tax records, and ambient concentrations were collected from a centrally-located ambient monitor.The contributions of ambient concentrations, local traffic and indoor sources to indoor concentrations were quantified with regression analyses. PM(2.5) was influenced less by local traffic but had significant indoor sources, while EC was associated with traffic and NO(2) with both traffic and indoor sources. Comparing models based on covariate selection using p-values or a Bayesian approach yielded similar results, with traffic density within a 50m buffer of a home and distance from a truck route as important contributors to indoor levels of NO(2) and EC, respectively. The Bayesian approach also highlighted the uncertanity in the models. We conclude that by utilizing public databases and focused questionnaire data we can identify important predictors of indoor concentrations for multiple air pollutants in a high-risk population.

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.

Sensitive topics, survey nonresponse, and considerations for interviewer training.

This paper discusses current challenges in achieving higher survey participation rates in random-digit-dial telephone surveys and proposes steps to address them through interviewer training to avoid refusals. It describes features of surveys that contribute to respondent reluctance to participate and offers a brief overview of current refusal aversion training methods to reduce nonresponse. It then identifies what challenges that unique features of random-digit-dial telephone surveys on sensitive topics might contribute to nonresponse. Recommendations are then proposed for changes in refusal aversion training, standard survey introductions, and informed consent procedures. Finally, further research is called for to identify which methods best balance the need to improve response rates with respondent safety and privacy in surveys with sensitive questions.

The air quality impacts of road closures associated with the 2004 Democratic National Convention in Boston.

BACKGROUND: The Democratic National Convention (DNC) in Boston, Massachusetts in 2004 provided an opportunity to evaluate the impacts of a localized and short-term but potentially significant change in traffic patterns on air quality, and to determine the optimal monitoring approach to address events of this nature. It was anticipated that the road closures associated with the DNC would both influence the overall air pollution level and the distribution of concentrations across the city, through shifts in traffic patterns. METHODS: To capture these effects, we placed passive nitrogen dioxide badges at 40 sites around metropolitan Boston before, during, and after the DNC, with the goal of capturing the array of hypothesized impacts. In addition, we continuously measured elemental carbon at three sites, and gathered continuous air pollution data from US EPA fixed-site monitors and traffic count data from the Massachusetts Highway Department. RESULTS: There were significant reductions in traffic volume on the highway with closures north of Boston, with relatively little change along other highways, indicating a more isolated traffic reduction rather than an across-the-board decrease. For our nitrogen dioxide samples, while there was a relatively small change in mean concentrations, there was significant heterogeneity across sites, which corresponded with our a priori classifications of road segments. The median ratio of nitrogen dioxide concentrations during the DNC relative to non-DNC sampling periods was 0.58 at sites with hypothesized traffic reductions, versus 0.88 for sites with no changes hypothesized and 1.15 for sites with hypothesized traffic increases. Continuous monitors measured slightly lower concentrations of elemental carbon and nitrogen dioxide during road closure periods at monitors proximate to closed highway segments, but not for PM2.5 or further from major highways. CONCLUSION: We conclude that there was a small but measurable influence of DNC-related road closures on air quality patterns in the Boston area, and that a low-cost monitoring study combining passive badges for spatial heterogeneity and continuous monitors for temporal heterogeneity can provide useful insight for community air quality assessments.